Public Sentiment and the State Standards
Unit 3 Case Study
Dr. Shaun Kellogg
January 3, 2022
1. PREPARE
Data sources such as digital learning environments and administrative data systems, as well as data produced by social media websites and the mass digitization of academic and practitioner publications, hold enormous potential to address a range of pressing problems in education, but collecting and analyzing text-based data also presents unique challenges. This week, our case study is guided by my colleague Josh Rosenberg’s recent article, Advancing new methods for understanding public sentiment about educational reforms: The case of Twitter and the Next Generation Science Standards.
We will focus on conducting a very simplistic “replication study” by comparing the sentiment of tweets about the Next Generation Science Standards (NGSS) and Common Core State Standards (CCSS) in order to better understand public reaction to these two curriculum reform efforts. Specifically, our Unit 3 case study will cover the following topics:
- Prepare: We’ll take a quick look at Dr. Rosenberg’s study, load packages we’ll need for analysis, and learn how to use Twitter’s API to retrieve data for those that created a Twitter Developer account.
- Wrangle: We focus on basic text mining processes such as text tokenization and stop word removal. Specifically, we will learn how to “tidy text” so we can perform some basic analyses such as retrieving word counts and term frequencies.
- Explore: In order to see what insight our data provides into answering our research questions, we will calculate some basic simple summary statistics from our tidied text and use data visualization to highlight some of these insights.
- Model: We learn about sentiment lexicons in our third lab and introduce the {vader} package to model the sentiment of tweets about the NGSS and CCSS state standards in order to better understand public reaction to these two curriculum reform efforts.
Communicate:
1a. Review the Literature
The Unit 3 Case Study: Public Sentiment and the State Standards is guided by a recent publication by (Rosenberg et al., 2021) Understanding Public Sentiment About Educational Reforms: The Next Generation Science Standards on Twitter. This study in turn builds on upon previous work by Wang & Fikis (2017) examining public opinion on the Common Core State Standards (CCSS) on Twitter. For Module 1, we will focus on analyzing tweets about the Next Generation Science Standards (NGSS) and Common Core State Standards (CCSS) in order to better understand key words and phrases that emerge, as well as public sentiment towards these two curriculum reform efforts.
Abstract
System-wide educational reforms are difficult to implement in the United States, but despite the difficulties, reforms can be successful, particularly when they are associated with broad public support. This study reports on the nature of the public sentiment expressed about a nationwide science education reform effort, the Next Generation Science Standards (NGSS). Through the use of data science techniques to measure the sentiment of posts on Twitter about the NGSS (N = 565,283), we found that public sentiment about the NGSS is positive, with only 11 negative posts for every 100 positive posts. In contrast to findings from past research and public opinion polling on the Common Core State Standards, sentiment about the NGSS has become more positive over time—and was especially positive for teachers. We discuss what this positive sentiment may indicate about the success of the NGSS in light of opposition to the Common Core State Standards.
Data Sources
Similar to data we’ll be using for this case study, Rosenberg et al. used publicly accessible data from Twitter collected using the Full-Archive Twitter API and the {rtweet} package in R. Specifically, the authors accessed tweets and user information from the hashtag-based #NGSSchat online community, all tweets that included any of the following phrases, with “/” indicating an additional phrase featuring the respective plural form: “ngss,” “next generation science standard/s,” “next gen science standard/s.”
Data used in this case was pulled using an Academic Research developer account and the {academictwitter} package, which uses the Twitter API v2 endpoints and allows researchers to access the full twitter archive, unlike the standard developer account. Data includes all tweets from January through May of 2020 and included the following terms: #ccss, common core, #ngsschat, ngss.
Below is an example of the code used to retrieve data for this lab. This code is set not to execute and will not run, but it does illustrate the search query used, variables selected, and time frame. For those that created a standard developer account, we will learn how to use your developer account later in this section to retrieve data from Twitter.
library(academictwitteR)
library(tidyverse)
ccss_tweets_2021 <-
get_all_tweets('(#commoncore OR "common core") -is:retweet lang:en',
"2021-01-01T00:00:00Z",
"2021-05-31T00:00:00Z",
bearer_token,
data_path = "ccss-data/",
bind_tweets = FALSE)
ccss_tweets <- bind_tweet_jsons(data_path = "ccss-data/") %>%
select(text,
created_at,
author_id,
id,
conversation_id,
source,
possibly_sensitive,
in_reply_to_user_id)
write_csv(ccss_tweets, here("data", "ccss-tweets.csv"))Analysis
The authors determined Tweet sentiment using the Java version of SentiStrength to assign tweets to two 5-point scales of sentiment, one for positivity and one for negativity, because SentiStrength is a validated measure for sentiment in short informal texts (Thelwall et al., 2011). In addition, they used this tool because Wang and Fikis (2019) used it to explore the sentiment of CCSS-related posts. We’ll be using the AFINN sentiment lexicon which also assigns words in a tweet to two 5-point scales, in addition to exploring some other sentiment lexicons to see if they produce similar results. We will use a similar approach to label tweets as positive, negative, or neutral using the {Vader} package which greatly simplifies this process.
The authors also used the lme4 package in R to run a mixed effects model to determine if sentiment changes over time and differs between teachers and non-teachers. We won’t try to replicate in this study, but we will take a look at some of their findings from this model in below.
Summary of Key Findings
- Contrasting with sentiment about CSSS, sentiment about the NGSS science education reform effort is overwhelmingly positive, with approximately 9 positive tweets for every negative tweet.
- Teachers were more positive than non-teachers, and sentiment became substantially more positive over the ten years of NGSS-related posts.
- Differences between the context of the tweets were small, but those that did not include the #NGSSchat hashtag became more positive over time than those posts that did not include the hashtag.
- Individuals posted more tweets during #NGSSchat chats, the sentiment of their posts was more positive, suggesting that while the context of individual tweets has a small effect (with posts not including the hashtag becoming more positive over time), the effect upon individuals of being involved in the #NGSSchat was positive.
Finally, you can watch Dr. Rosenberg provide a quick 3-minute overview of this work at <https://stanford.app.box.com/s/i5ixkj2b8dyy8q5j9o5ww4nafznb497x>
1b. Define Questions
One overarching question that Silge and Robinson (2018) identify as a central question to text mining and natural language processing, and that we’ll explore later in this case study, is the question:
How do we to quantify what a document or collection of documents is about?
The questions guiding the Rosenberg et al. study attempt to quantify public sentiment around the NGSS and how that sentiment changes over time. Specifically, they asked:
- What is the public sentiment expressed toward the NGSS?
- How does sentiment for teachers differ from non-teachers?
- How do tweets posted to #NGSSchat differ from those without the hashtag?
- How does participation in #NGSSchat relate to the public sentiment individuals express?
- How does public sentiment vary over time?
For our text mining case study, we’ll use approaches similar to those used by the authors cited above to better understand public discourse surrounding these standards, particularly as they relate to STEM education. We will also try to guage public sentiment around the NGSS, by comparing how much more positive or negative NGSS tweets are relative to CSSS tweets. Specifically, in this case study we’ll attempt to answer the following questions:
- What are the most frequent words or phrases used in reference to tweets about the CCSS and NGSS?
- How does sentiment for NGSS compare to sentiment for CCSS?
1c. Load Libraries
tidytext 📦
As we’ll learn first hand in this module, using tidy data principles can also make many text mining tasks easier, more effective, and consistent with tools already in wide use. The {tidytext} package helps to convert text into data frames of individual words, making it easy to to manipulate, summarize, and visualize text using using familiar functions form the {tidyverse} collection of packages.
Let’s go ahead and load the {tidytext} package:
library(tidytext)For a more comprehensive introduction to the tidytext package, I cannot recommend enough the free and excellent online book, Text Mining with R: A Tidy Approach (Silge & Robinson, 2017). If you’re interested in pursuing text analysis using R post Summer Workshop, this will be a go to reference.
The vader Package 📦
The {vader} package is for the Valence Aware Dictionary for sEntiment Reasoning (VADER), a rule-based model for general sentiment analysis of social media text and specifically attuned to measuring sentiment in microblog-like contexts.
To learn more about the {vader} package and its development, take a look at the article by Hutto and Gilbert (2014), VADER: A Parsimonious Rule-based Model forSentiment Analysis of Social Media Text.
Let’s go ahead and load the VADER library:
library(vader)Note: The {vader} package can take quite some time to run on a large datasets like the one we’ll be working with, so in our Model section we will examine just a small(ish) subset of tweets.
rtweet 📦 (Optional)
The {rtweet} package provides users a range of functions designed to extract data from Twitter’s REST and streaming APIs and has three main goals:
Formulate and send requests to Twitter’s REST and stream APIs.
Retrieve and iterate over returned data.
Wrangling data into tidy structures.
For those that created a Twitter Developer account, load the {rtweet} package that we’ll be using to accomplish all three of the goals listed above:
library(rtweet)Other Packages
Finally, there are a few other packages we’ll need to get started. The first two should look familiar while third {wordcloud2} package is handy little package for creating interactive word clouds.
library(tidyverse)## ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.1 ──## ✓ ggplot2 3.3.5 ✓ purrr 0.3.4
## ✓ tibble 3.1.5 ✓ dplyr 1.0.7
## ✓ tidyr 1.1.4 ✓ stringr 1.4.0
## ✓ readr 2.0.2 ✓ forcats 0.5.1## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## x dplyr::filter() masks stats::filter()
## x purrr::flatten() masks rtweet::flatten()
## x dplyr::lag() masks stats::lag()library(here)## here() starts at /cloud/projectlibrary(wordcloud2)2. WRANGLE
The importance of data wrangling, particularly when working with text, is difficult to overstate. Just as a refresher, wrangling involves the initial steps of going from raw data to a dataset that can be explored and modeled (Krumm et al., 2018). This case study will place a heavy emphasis on preparing text for analysis and in particular we’ll learn how to:
- Import Tweets. First we revisit the familiar
read_csv()function for reading in our CCSS and NGSS tweets into R. For those of you who created a Twitter Developer Account, we’ll also demonstrate the use of the {rtweet} package for downloading data directly from Twitter. - Restructure Data. We focus on removing extraneous data using the
select()andfilter()functions from {dplyr}, and revisit functions from the Tidy Your Data Primer for merging data frames. - Tidy Text. Finally, we introduce the {tidytext} package to “tidy” and tokenize our tweets in order to create our data frame for analysis. We also introduce a new join function to remove “stop words” that don’t add much value to our analysis.
2a. Part 1: Import Tweets from API (Optional)
This section is optional and for those those setup a Twitter Developer Account and App. The Import Tweets section introduces the following functions from the {rtweet} package for reading Twitter data into R:
create_token()Sends request to generate authorization tokens for use of the Twitter API.search_tweets()Pulls up to 18,000 tweets from the last 6-9 days matching provided search terms.search_tweets2()Returns data from multiple search queries.get_timelines()Returns up to 3,200 tweets of one or more specified Twitter users.
Authorization methods
Users can create their personal Twitter token in two different ways. THe recommend method is outlined below.
- Navigate to developer.twitter.com/en/apps and select your Twitter app
- Click the tab labeled
Keys and tokensto retrieve your keys. - Locate the
Consumer API keys(aka “API Secret”).
- Scroll down to
Access token & access token secretand clickCreate
- Copy and paste the four keys (along with the name of your app) into an R script file and pass them along to
create_token(). Note, these keys are named secret for a reason. I recommend setting up your token in a separate R script than the one that you will eventually share.
## store api keys (these are fake example values; replace with your own keys)
app_name <- "Educational Text Mining"
api_key <- "tTnrXa68RRRJYB9ECBLVziVrX"
api_secret_key <- "48Lef96R1dT6dtRHnDsgt1GelrdGrWDf23ahUY14VBImvMppY0"
access_token <- "1671928994-DWrq7PRiJBhBHlJ7MoxZpNdi8kvSJS22K3PTJab"
access_token_secret <- "BnQ9fcCLXp95gNWzC6VWoggIW8WYEq8B6o1ytZatNZcNF"
## authenticate via web browser
token <- create_token(
app = app_name,
consumer_key = api_key,
consumer_secret = api_secret_key,
access_token = access_token,
access_secret = access_token_secret)Search Tweets
Since one of our goals for this walkthrough is a very crude replication of the study by Rosenberg et al. (2021), let’s begin by introducing the search_tweets() function to try reading into R 5,000 tweets containing the NGSS hashtag and store as a new data frame ngss_all_tweets.
Type or copy the following code into your R script or console and run:
ngss_all_tweets <- search_tweets(q = "#NGSSchat", n=5000)Note that the first argument q = that the search_tweets() function expects is the search term included in quotation marks and that n = specifies the maximum number of tweets
Your Turn ⤵
View your new ngss_all_tweetsdata frame using the code chunk below and answer the following questions:
View(ngss_all_tweets)How many tweets did our query using the Twitter API actually return? How many variables?
- 285 tweets with 90 variables
Why do you think our query pulled in far less than 5,000 tweets requested?
- The query only goes back to 20 October. Using the ?search_tweets help function shows that this function limits the query to the past 6-9 days.
Does our query also include retweets? How do you know?
- Yes. There is a variable ‘is_retweet’ in the dataset with values of TRUE/FALSE.
Remove Retweets
While not explicitly mentioned in the paper, it’s likely the authors removed retweets in their query since a retweet is simply someone else reposting someone else’s tweet and would duplicate the exact same content of the original.
Let’s use the include_rts = argument to remove any retweets by setting it to FALSE:
ngss_non_retweets <- search_tweets("#NGSSchat",
n=5000,
include_rts = FALSE)Using the OR Operator
If you recall from the Data Sources section above, the authors accessed tweets and user information from the hashtag-based #NGSSchat online community, all tweets that included any of the following phrases, with “/” indicating an additional phrase featuring the respective plural form: “ngss,” “next generation science standard/s,” “next gen science standard/s.”
Let’s modify our query using the OR operator to also include “ngss” so it will return tweets containing either #NGSSchat or “ngss” and assign to ngss_or_tweets:
ngss_or_tweets <- search_tweets(q = "#NGSSchat OR ngss",
n=5000,
include_rts = FALSE)Your Turn ⤵
Try including both search terms but excluding the OR operator to answer the following question:
ngsschat_ngss_tweets <- search_tweets(q = "NGSSchat ngss",
n = 5000,
type = "mixed",
include_rts = FALSE)Does excluding the
ORoperator return more tweets, the same number of tweets, or fewer tweets? Why?- Fewer. It’s looking for both terms in a single tweet.
What other useful arguments does the
search_tweet()function contain? Try adding one and see what happens.I tried type = “popular” and got no returns. “Mixed” came back with my original 26 tweets using both search terms. “Popular” was searching for tweets that hit the magic AI threshold for being popular, which none of these tweets did. “Mixed” brings in a mixture of both “popular” and unpopular? tweets.
search_tweets( q, n = 100, type = "recent", include_rts = TRUE, geocode = NULL, max_id = NULL, parse = TRUE, token = NULL, retryonratelimit = FALSE, verbose = TRUE, ... )
Hint: Use the ?search_tweets help function to learn more about the q argument and other arguments for composing search queries.
Use Multiple Queries
Unfortunately, the OR operator will only get us so far. In order to include the additional search terms, we will need to use the c() function to combine our search terms into a single list.
The rtweets package has an additional search_tweets2() function for using multiple queries in a search. To do this, either wrap single quotes around a search query using double quotes, e.g., q = '"next gen science standard"' or escape each internal double quote with a single backslash, e.g., q = "\"next gen science standard\"".
Copy and past the following code to store the results of our query in ngss_tweets:
ngss_tweets <- search_tweets2(c("#NGSSchat OR ngss",
'"next generation science standard"',
'"next generation science standards"',
'"next gen science standard"',
'"next gen science standards"' ),
n=5000,
include_rts = FALSE)Our First Dictionary
Recall that for our research question we wanted to compare public sentiment about both the NGSS and CCSS state standards. Let’s go ahead and create our very first “dictionary” for identifying tweets related to either set of standards, and then use that dictionary for our the q = query argument to pull tweets related to the state standards.
To do so, we’ll need to add some additional search terms to our list:
ngss_dictionary <- c("#NGSSchat OR ngss",
'"next generation science standard"',
'"next generation science standards"',
'"next gen science standard"',
'"next gen science standards"')
ngss_tweets <- search_tweets2(ngss_dictionary, n=5000, include_rts = FALSE)Now let’s create a dictionary for the Common Core State Standards and pass that to our search_tweets() function to get the most recent tweets:
ccss_dictionary <- c("#commoncore", '"common core"')
ccss_tweets <-
ccss_dictionary %>%
search_tweets2(n=5000, include_rts = FALSE)Notice that you can use the pipe operator with the search_tweets() function just like you would other functions from the tidyverse.
Your Turn ⤵
Use the
search_tweetsfunction to create you own custom query for a twitter hashtag or topic(s) of interest.educhat_dictionary <- c("#educhat", "#edutwitter") educhat_tweets <- educhat_dictionary %>% search_tweets2(n = 5000, include_rts = FALSE)
Other Useful Queries
For your independent analysis, you may be interest in exploring posts by specific users rather than topics, key words, or hashtags. Yes, there is a function for that too!
For example, let’s create another list containing the usernames of me and some of my colleagues at the Friday Institute using the c() function again and use the get_timelines() function to get the most recent tweets from each of those users:
fi <- c("sbkellogg", "TooSweetGeek", "haspires", "tarheel93", "drcallie_tweets", "AlexDreier")
fi_tweets <- fi %>% get_timelines(include_rts=FALSE)And let’s use the sample_n() function from the dplyr package to pick 10 random tweets and use select() to select and view just the screenname and text columns that contains the user and the content of their post:
sample_n(fi_tweets, 10) %>% select(screen_name, text)## # A tibble: 10 × 2
## screen_name text
## <chr> <chr>
## 1 drcallie_tweets "Writing up the methodology for a grant proposal and totally…
## 2 tarheel93 "@sayscye @VanceCoSchools Congratulations and well deserved!"
## 3 sbkellogg "@jrosenberg6432 Not sure if one exists but we can start one…
## 4 TooSweetGeek "@Sea_likedaocean eh huh... eh huh... I understood the assig…
## 5 haspires "CCMMS Student leaders in the house! @FridayInstitute @CCMMS…
## 6 TooSweetGeek "@wes_wade Glad to hear she had a great day! Not so glad to …
## 7 drcallie_tweets "@Dr_JVandenberg Aww, you are so kind! 🤗 Thank you so much!…
## 8 AlexDreier "@MindShiftKQED @anya1anya This was really confusing before …
## 9 TooSweetGeek "@SeeStephScience Congrats!! 🎉🎉🎉 https://t.co/CJrSADRjfo"
## 10 drcallie_tweets "@JemiliaDavis Congratulations!! 🎉🎊"We’ve only scratched the surface of the number of functions available in the rtweets package for searching Twitter. Use the following function to learn more about the {rtweet} package:
vignette("intro", package="rtweet")Your Turn ⤵
To conclude Section 2a, try one of the following search functions from the rtweet vignette:
get_timelines()Get the most recent 3,200 tweets from users.stream_tweets()Randomly sample (approximately 1%) from the live stream of all tweets.get_friends()Retrieve a list of all the accounts a user follows.get_followers()Retrieve a list of the accounts following a user.get_favorites()Get the most recently favorited statuses by a user.get_trends()Discover what’s currently trending in a city.search_users()Search for 1,000 users with the specific hashtag in their profile bios.get_trends(woeid = "Raleigh")## # A tibble: 49 × 9 ## trend url promoted_content query tweet_volume place woeid ## <chr> <chr> <lgl> <chr> <int> <chr> <int> ## 1 First Mo… http://twitte… NA %22First… 33242 Rale… 2.48e6 ## 2 #MondayM… http://twitte… NA %23Monda… NA Rale… 2.48e6 ## 3 Ashli Ba… http://twitte… NA %22Ashli… NA Rale… 2.48e6 ## 4 #MondayM… http://twitte… NA %23Monda… 64336 Rale… 2.48e6 ## 5 #PMSIDon… http://twitte… NA %23PMSID… NA Rale… 2.48e6 ## 6 #snowday http://twitte… NA %23snowd… NA Rale… 2.48e6 ## 7 #ARMYSel… http://twitte… NA %23ARMYS… 183819 Rale… 2.48e6 ## 8 Weeknd http://twitte… NA Weeknd 32530 Rale… 2.48e6 ## 9 Abel http://twitte… NA Abel 22237 Rale… 2.48e6 ## 10 Dawn FM http://twitte… NA %22Dawn+… 33611 Rale… 2.48e6 ## # … with 39 more rows, and 2 more variables: as_of <dttm>, created_at <dttm>
2a. Part 2: Import Tweets from CSV
First, let’s use the by now familiar read_csv() and here() functions to import our ccss_tweets.csv file saved in our data folder:
ccss_tweets <- read_csv(here("unit-3", "data", "ccss-tweets.csv"),
col_types = cols(author_id = col_character(),
id = col_character(),
conversation_id = col_character(),
in_reply_to_user_id = col_character()
)
)## Warning: One or more parsing issues, see `problems()` for detailsNote the addition of the col_types = argument for changing some of the column types to character strings because the numbers for those particular columns actually indicate identifiers for authors and tweets:
author_id= the author of the tweetid= the unique id for each tweetconverastion_id= the unique id for each conversation threadin_reply_to_user_id= the author of the tweet being replied to
Your Turn ⤵
Use the following code chunk to import the NGSS tweets located in the same data folder as our common core tweets. By default, R will treat numerical IDs in our dataset as numeric values but we will need to convert these to characters like demonstrated above for the purpose of analysis:
ngss_tweets <- read_csv(here("unit-3", "data", "ngss-tweets.csv"),
col_types = cols(author_id = col_character(),
id = col_character(),
conversation_id = col_character(),
in_reply_to_user_id = col_character()
)
)## Warning: One or more parsing issues, see `problems()` for detailsImporting data and dealing with data types can be a bit tricky, especially for beginners. Recall from previous case studies that RStudio has an “Import Dataset” feature in the Environment Pane that can help you use the {readr} package and associated functions to greatly facilitate this process. If you get stuck, you can copy the code generated in the lower right hand corner of the Import Dataset window.
Now use the following code chunk to inspect the head() of each data frame and answer the questions that follow:
head(ccss_tweets)## # A tibble: 6 × 8
## text created_at author_id id conversation_id source
## <chr> <dttm> <chr> <chr> <chr> <chr>
## 1 "@catturd2 Hmm… 2021-01-02 00:49:28 1609854356 13451… 13451697062071… Twitte…
## 2 "@homebrew1500… 2021-01-02 00:40:05 1249594897… 13451… 13451533915976… Twitte…
## 3 "@ClayTravis D… 2021-01-02 00:32:46 8877070540… 13451… 13450258639942… Twitte…
## 4 "@KarenGunby @… 2021-01-02 00:24:01 1249594897… 13451… 13451533915976… Twitte…
## 5 "@keith3048 I … 2021-01-02 00:23:42 1252747591 13451… 13451533915976… Twitte…
## 6 "Probably comm… 2021-01-02 00:18:38 1276017320… 13451… 13451625486818… Twitte…
## # … with 2 more variables: possibly_sensitive <lgl>, in_reply_to_user_id <chr>head(ngss_tweets)## # A tibble: 6 × 8
## text created_at author_id id conversation_id source
## <chr> <dttm> <chr> <chr> <chr> <chr>
## 1 "Please help u… 2021-01-06 00:50:49 3279907796 13466… 13466201998945… Twitte…
## 2 "What lab mate… 2021-01-06 00:45:32 1010324664… 13466… 13466188701325… Hootsu…
## 3 "I recently sa… 2021-01-06 00:39:37 61829645 13466… 13466173820858… Twitte…
## 4 "I'm thrilled … 2021-01-06 00:30:13 461653415 13466… 13466150172071… Twitte…
## 5 "PLS RT. Excit… 2021-01-06 00:15:05 22293234 13466… 13466112069671… Twitte…
## 6 "Inspired by M… 2021-01-06 00:00:00 3317960226 13466… 13466074140999… TweetD…
## # … with 2 more variables: possibly_sensitive <lgl>, in_reply_to_user_id <chr>Wow, so much for a family friendly case study! Based on this very limited sample, which set of standards do you think Twitter users are more negative about?
- Common Core Science Standards is the more negative query.
Let’s take a slightly larger sample of the CCSS tweets:
ccss_tweets %>%
sample_n(20) %>%
relocate(text)## # A tibble: 20 × 8
## text created_at author_id id conversation_id source
## <chr> <dttm> <chr> <chr> <chr> <chr>
## 1 "@philthatrem… 2021-03-10 05:03:37 1133894673… 13695… 13695138698535… Twitte…
## 2 "Just had a c… 2021-02-25 20:32:55 112487884 13650… 13650370790135… Twitte…
## 3 "@VeBo1991 WT… 2021-02-19 00:29:57 1265700973… 13625… 13625559333695… Twitte…
## 4 "The answer: … 2021-01-30 19:21:16 629884158 13555… 13555969643860… Twitte…
## 5 "NOTE: The or… 2021-05-17 13:53:52 9084981513… 13942… 11352508458162… Twitte…
## 6 "Common core … 2021-02-16 15:19:11 363296736 13616… 13616966356219… Twitte…
## 7 "And here in … 2021-04-08 18:47:30 8055708023… 13802… 13802308391544… Twitte…
## 8 "@notcapnamer… 2021-01-19 23:57:34 22844473 13516… 13516500890565… Twitte…
## 9 "@taylorbilt … 2021-05-16 00:50:01 7501350931… 13937… 13936508918494… Twitte…
## 10 "⚘ EPUB Free … 2021-05-15 02:15:21 1390573333… 13933… 13933895072322… Twitte…
## 11 "@AmberRose2d… 2021-04-06 21:54:12 175150086 13795… 13794730873352… Twitte…
## 12 "CSO: Aspira … 2021-03-04 22:11:32 1648600094 13675… 13675986099030… Twitte…
## 13 "@SJonNantuck… 2021-04-18 16:37:39 1186858346 13838… 13834886458232… Twitte…
## 14 "Public educa… 2021-03-18 22:22:22 1231450284… 13726… 13726747690662… Twitte…
## 15 "Common Core … 2021-05-07 18:36:09 18945736 13907… 13907372310909… Twitte…
## 16 "Kindle Free … 2021-02-19 08:13:12 1355023198… 13626… 13626765945783… Twitte…
## 17 "Volume 2: Ze… 2021-03-24 18:43:15 610065715 13747… 13747860925960… Twitte…
## 18 "Right about … 2021-01-25 21:31:52 3260741186 13538… 13538178900346… Twitte…
## 19 "@IAmMonique1… 2021-02-28 01:21:14 1324026979 13658… 13657645626797… Twitte…
## 20 "Common Core … 2021-05-30 01:58:27 1386161359… 13988… 13988210733623… WordPr…
## # … with 2 more variables: possibly_sensitive <lgl>, in_reply_to_user_id <chr>Your Turn ⤵
Use the code chunk below to take a sample of the NGSS tweets. Try to do it without looking at the code above first:
ngss_tweets %>%
sample_n(20) %>%
relocate(text)## # A tibble: 20 × 8
## text created_at author_id id conversation_id source
## <chr> <dttm> <chr> <chr> <chr> <chr>
## 1 "@SciEducator… 2021-01-06 11:45:43 136014942 13467… 13466475496633… Twitte…
## 2 "Testimonial … 2021-05-18 15:08:31 3097084436 13946… 13946712467098… Twitte…
## 3 "@lekadegroot… 2021-02-22 01:32:00 2563921782 13636… 13636618423299… TweetD…
## 4 "RT Political… 2021-04-10 07:00:51 251436481 13807… 13807777801205… IFTTT
## 5 "@HeatherARic… 2021-04-14 23:40:38 14358343 13824… 13824709607421… Twitte…
## 6 "Working on N… 2021-04-29 19:46:27 7852785077… 13878… 13878558229281… Twitte…
## 7 "Here is Wedn… 2021-05-11 13:26:57 872707136 13921… 13921089728790… Twitte…
## 8 "Calling all … 2021-01-16 19:08:34 20546506 13505… 13505203371008… Twitte…
## 9 "@ngss_offici… 2021-02-15 09:26:35 8586447763… 13612… 13612388070146… Twitte…
## 10 "Join us toni… 2021-03-24 20:25:31 1449382200 13748… 13748196876919… Twitte…
## 11 "A4: Trying t… 2021-05-21 01:31:41 41819846 13955… 13955528489816… TweetD…
## 12 "Great point … 2021-05-21 01:54:10 558971700 13955… 13955585038975… TweetD…
## 13 "Spending thi… 2021-03-20 14:22:26 20482401 13732… 13732787631532… Twitte…
## 14 "Don't miss t… 2021-03-03 17:15:15 44159179 13671… 13671616623699… Hootsu…
## 15 "NGSS in acti… 2021-05-22 02:28:17 2273635742 13959… 13959294785476… Twitte…
## 16 "@ChienforSTE… 2021-01-22 02:09:36 558971700 13524… 13524378346411… TweetD…
## 17 "Getting the … 2021-03-16 22:54:27 1195148655… 13719… 13719580662712… Twitte…
## 18 "Elizabeth jo… 2021-01-08 02:04:20 2817268495 13473… 13473634775229… TweetD…
## 19 "A3 #NGSS &am… 2021-02-19 01:22:39 3164721571 13625… 13625732784438… TweetD…
## 20 "We're dedica… 2021-05-28 17:57:52 1158772852… 13983… 13983377428455… Twitte…
## # … with 2 more variables: possibly_sensitive <lgl>, in_reply_to_user_id <chr>Still of the same opinion?
- Yes, these appear much less negative.
What else you notice about our data sets? Record a few observations that you think are relevant to our analysis or might be useful for future analyses.
CCSS tweets begin with hashtags or userIDs while the NGSS tweets look save those features until the end of the tweet.
The negative tweets link to a variety of topics (like politics) not necessarily just common core science.
What questions do you have about these data sets? What are you still curious about?
- There are some symbols that exist that confuse me. in the first CCSS tweet. Is that how a specific character is represented when the .csv is imported? Looking at the original tweet, only a space is there and no character.
2b. Restructure Data
Subset Tweets
As you may have noticed, we have more data than we need for our analysis and should probably pare it down to just what we’ll use.
Let’s start with the CCSS tweets first. And since this is a family friendly case study, let’s use the filter() function introduced in previous labs to filter out rows containing “possibly sensitive” language:
ccss_tweets_1 <- ccss_tweets %>%
filter(possibly_sensitive == "FALSE")Now let’s use the select() function to select the following columns from our new ss_tweets_clean data frame:
textcontaining the tweet which is our primary data source of interestauthor_idof the user who created the tweetcreated_attimestamp for examining changes in sentiment over timeconversation_idfor examining sentiment by conversationsidfor the unique reference id for each tweet and useful for counts
ccss_tweets_2 <- ccss_tweets_1 %>%
select(text,
author_id,
created_at,
conversation_id,
id)Your Turn ⤵
Note: The select() function will also reorder your columns based on the order in which you list them.
Use the code chunk below to reorder the columns to your liking and assign to ccss_tweets_3:
ccss_tweets_3 <- ccss_tweets_2 %>%
select(text,
created_at,
id,
author_id,
conversation_id)Add & Relocate Columns
Finally, since we are interested in comparing the sentiment of NGSS tweets with CCSS tweets, it would be helpful if we had a column to quickly identify the set of state standards with which each tweet is associated.
We’ll use the mutate() function to create a new variable called standards to label each tweets as “ccss”:
ccss_tweets_4 <- mutate(ccss_tweets_2, standards = "ccss")
colnames(ccss_tweets_4)## [1] "text" "author_id" "created_at" "conversation_id"
## [5] "id" "standards"And just because it bothers me, I’m going to use the relocate() function to move the standards column to the first position so I can quickly see which standards the tweet is from:
ccss_tweets_5 <- relocate(ccss_tweets_4, standards)
colnames(ccss_tweets_5)## [1] "standards" "text" "author_id" "created_at"
## [5] "conversation_id" "id"Again, we could also have used the select() function to reorder columns like so:
ccss_tweets_5 <- ccss_tweets_4 %>%
select(standards,
text,
author_id,
created_at,
conversation_id,
id)
colnames(ccss_tweets_5)## [1] "standards" "text" "author_id" "created_at"
## [5] "conversation_id" "id"Before moving on to the CCSS standards, let’s use the %>% operator and rewrite the code from our wrangling so there is less redundancy and it is easier to read:
# Search Tweets
ccss_tweets_clean <- ccss_tweets %>%
filter(possibly_sensitive == "FALSE") %>%
select(text, author_id, created_at, conversation_id, id) %>%
mutate(standards = "ccss") %>%
relocate(standards)
head(ccss_tweets_clean)## # A tibble: 6 × 6
## standards text author_id created_at conversation_id id
## <chr> <chr> <chr> <dttm> <chr> <chr>
## 1 ccss "@catturd2 H… 1609854356 2021-01-02 00:49:28 13451697062071… 13451…
## 2 ccss "@homebrew15… 1249594897… 2021-01-02 00:40:05 13451533915976… 13451…
## 3 ccss "@ClayTravis… 8877070540… 2021-01-02 00:32:46 13450258639942… 13451…
## 4 ccss "@KarenGunby… 1249594897… 2021-01-02 00:24:01 13451533915976… 13451…
## 5 ccss "@keith3048 … 1252747591 2021-01-02 00:23:42 13451533915976… 13451…
## 6 ccss "Probably co… 1276017320… 2021-01-02 00:18:38 13451625486818… 13451…Your Turn ⤵
Recall from section 1b. Define Questions that we are interested in comparing word usage and public sentiment around both the Common Core and Next Gen Science Standards.
Create an new ngss_tweets_clean data frame consisting of the Next Generation Science Standards tweets we imported by using the code above as a guide.
ngss_tweets_clean <- ngss_tweets %>%
filter(possibly_sensitive == "FALSE") %>%
select(text, author_id, created_at, conversation_id, id) %>%
mutate(standards = "ngss") %>%
relocate(standards)
head(ngss_tweets_clean)## # A tibble: 6 × 6
## standards text author_id created_at conversation_id id
## <chr> <chr> <chr> <dttm> <chr> <chr>
## 1 ngss "Please help… 3279907796 2021-01-06 00:50:49 13466201998945… 13466…
## 2 ngss "What lab ma… 1010324664… 2021-01-06 00:45:32 13466188701325… 13466…
## 3 ngss "I recently … 61829645 2021-01-06 00:39:37 13466173820858… 13466…
## 4 ngss "I'm thrille… 461653415 2021-01-06 00:30:13 13466150172071… 13466…
## 5 ngss "PLS RT. Exc… 22293234 2021-01-06 00:15:05 13466112069671… 13466…
## 6 ngss "Inspired by… 3317960226 2021-01-06 00:00:00 13466074140999… 13466…Merge Data Frames
Finally, let’s combine our CCSS and NGSS tweets into a single data frame by using the union() function from dplyr and simply supplying the data frames that you want to combine as arguments:
ss_tweets <- union(ccss_tweets_clean,
ngss_tweets_clean)
ss_tweets## # A tibble: 35,233 × 6
## standards text author_id created_at conversation_id id
## <chr> <chr> <chr> <dttm> <chr> <chr>
## 1 ccss "@catturd2 H… 1609854356 2021-01-02 00:49:28 13451697062071… 13451…
## 2 ccss "@homebrew15… 124959489… 2021-01-02 00:40:05 13451533915976… 13451…
## 3 ccss "@ClayTravis… 887707054… 2021-01-02 00:32:46 13450258639942… 13451…
## 4 ccss "@KarenGunby… 124959489… 2021-01-02 00:24:01 13451533915976… 13451…
## 5 ccss "@keith3048 … 1252747591 2021-01-02 00:23:42 13451533915976… 13451…
## 6 ccss "Probably co… 127601732… 2021-01-02 00:18:38 13451625486818… 13451…
## 7 ccss "@LisaS4680 … 922132923… 2021-01-02 00:16:11 13451595466087… 13451…
## 8 ccss "@JerryGl291… 122016089… 2021-01-02 00:10:29 13447179758914… 13451…
## 9 ccss "@JBatNC304 … 880914489… 2021-01-02 00:09:15 13447403608625… 13451…
## 10 ccss "@chiefaugur… 124959489… 2021-01-01 23:54:38 13451533915976… 13451…
## # … with 35,223 more rowsNote that when creating a “union” like this (i.e. stacking one data frame on top of another), you should have the same number of columns in each data frame and they should be in the exact same order.
Alternatively, we could have used the bind_rows() function from {dplyr} as well:
ss_tweets <- bind_rows(ccss_tweets_clean,
ngss_tweets_clean)
ss_tweets## # A tibble: 35,233 × 6
## standards text author_id created_at conversation_id id
## <chr> <chr> <chr> <dttm> <chr> <chr>
## 1 ccss "@catturd2 H… 1609854356 2021-01-02 00:49:28 13451697062071… 13451…
## 2 ccss "@homebrew15… 124959489… 2021-01-02 00:40:05 13451533915976… 13451…
## 3 ccss "@ClayTravis… 887707054… 2021-01-02 00:32:46 13450258639942… 13451…
## 4 ccss "@KarenGunby… 124959489… 2021-01-02 00:24:01 13451533915976… 13451…
## 5 ccss "@keith3048 … 1252747591 2021-01-02 00:23:42 13451533915976… 13451…
## 6 ccss "Probably co… 127601732… 2021-01-02 00:18:38 13451625486818… 13451…
## 7 ccss "@LisaS4680 … 922132923… 2021-01-02 00:16:11 13451595466087… 13451…
## 8 ccss "@JerryGl291… 122016089… 2021-01-02 00:10:29 13447179758914… 13451…
## 9 ccss "@JBatNC304 … 880914489… 2021-01-02 00:09:15 13447403608625… 13451…
## 10 ccss "@chiefaugur… 124959489… 2021-01-01 23:54:38 13451533915976… 13451…
## # … with 35,223 more rowsThe distinction between these two functions is that union by default removes any duplicate rows that might have shown up in our queries.
However, since both functions returned the same number of rows, it’s clear we do not have any duplicates. If we wanted to verify, {dplyr} also has an intersect function to merge the two data frames, but only where they intersect(), or where they have duplicate rows.
ss_tweets_duplicate <- intersect(ccss_tweets_clean,
ngss_tweets_clean)Your Turn ⤵
Finally, let’s take a quick look at both the head() and the tail() of this new ss_tweets data frame to make sure it contains both “ngss” and “ccss” standards:
head(ss_tweets)## # A tibble: 6 × 6
## standards text author_id created_at conversation_id id
## <chr> <chr> <chr> <dttm> <chr> <chr>
## 1 ccss "@catturd2 H… 1609854356 2021-01-02 00:49:28 13451697062071… 13451…
## 2 ccss "@homebrew15… 1249594897… 2021-01-02 00:40:05 13451533915976… 13451…
## 3 ccss "@ClayTravis… 8877070540… 2021-01-02 00:32:46 13450258639942… 13451…
## 4 ccss "@KarenGunby… 1249594897… 2021-01-02 00:24:01 13451533915976… 13451…
## 5 ccss "@keith3048 … 1252747591 2021-01-02 00:23:42 13451533915976… 13451…
## 6 ccss "Probably co… 1276017320… 2021-01-02 00:18:38 13451625486818… 13451…tail(ss_tweets)## # A tibble: 6 × 6
## standards text author_id created_at conversation_id id
## <chr> <chr> <chr> <dttm> <chr> <chr>
## 1 ngss @BK3DSci Bria… 558971700 2021-05-21 01:10:28 13955471161272… 13955…
## 2 ngss A1 My studen… 1449382200 2021-05-21 01:10:20 13955474728990… 13955…
## 3 ngss A1: It is an … 136014942 2021-05-21 01:09:58 13955473807585… 13955…
## 4 ngss @MsB_Reilly M… 3164721571 2021-05-21 01:09:54 13955471085775… 13955…
## 5 ngss A1.5 I also l… 14449947 2021-05-21 01:09:46 13955473306029… 13955…
## 6 ngss @MsB_Reilly W… 558971700 2021-05-21 01:09:44 13955471085775… 13955…2c. Tidy Text
Text data by it’s very nature is ESPECIALLY untidy and is sometimes referred to as “unstructured” data. In this section we learn some very useful functions from the {tidytext} package to convert text to and from tidy formats. Having our text in a tidy format will allow us to switch seamlessly between tidy tools and existing text mining packages, while also making it easier to visualize text summaries in other data analysis tools like Tableau.
Tokenize Text
In Chapter 1 of Text Mining with R, Silge & Robinson (2017) define the tidy text format as a table with one-token-per-row, and explain that:
A token is a meaningful unit of text, such as a word, two-word phrase (bigram), or sentence that we are interested in using for analysis. And tokenization is the process of splitting text into tokens.
This one-token-per-row structure is in contrast to the ways text is often stored for text analysis, perhaps as strings in a corpus object or in a document-term matrix. For tidy text mining, the token that is stored in each row is most often a single word, but can also be an n-gram, sentence, or paragraph.
For this part of our workflow, our goal is to transform our ss_tweets data from this:
head(relocate(ss_tweets, text))## # A tibble: 6 × 6
## text standards author_id created_at conversation_id id
## <chr> <chr> <chr> <dttm> <chr> <chr>
## 1 "@catturd2 H… ccss 1609854356 2021-01-02 00:49:28 13451697062071… 13451…
## 2 "@homebrew15… ccss 1249594897… 2021-01-02 00:40:05 13451533915976… 13451…
## 3 "@ClayTravis… ccss 8877070540… 2021-01-02 00:32:46 13450258639942… 13451…
## 4 "@KarenGunby… ccss 1249594897… 2021-01-02 00:24:01 13451533915976… 13451…
## 5 "@keith3048 … ccss 1252747591 2021-01-02 00:23:42 13451533915976… 13451…
## 6 "Probably co… ccss 1276017320… 2021-01-02 00:18:38 13451625486818… 13451…Into a “tidy text” one-token-per-row format that looks like this:
tidy_tweets <- ss_tweets %>%
unnest_tokens(output = word,
input = text) %>%
relocate(word)
head(tidy_tweets)## # A tibble: 6 × 6
## word standards author_id created_at conversation_id id
## <chr> <chr> <chr> <dttm> <chr> <chr>
## 1 catturd2 ccss 1609854356 2021-01-02 00:49:28 1345169706207109120 1345170…
## 2 hmmmm ccss 1609854356 2021-01-02 00:49:28 1345169706207109120 1345170…
## 3 common ccss 1609854356 2021-01-02 00:49:28 1345169706207109120 1345170…
## 4 core ccss 1609854356 2021-01-02 00:49:28 1345169706207109120 1345170…
## 5 math ccss 1609854356 2021-01-02 00:49:28 1345169706207109120 1345170…
## 6 now ccss 1609854356 2021-01-02 00:49:28 1345169706207109120 1345170…If you take ECI 588: Text Mining in Education, you’ll learn about other data structures for text analysis like the document-term matrix and corpus objects. For now, however, working with the familiar tidy data frame allows us to take advantage of popular packages that use the shared tidyverse syntax and principles for wrangling, exploring, and modeling data.
As demonstrated above, the tidytext package provides the incredibly powerful unnest_tokens() function to tokenize text (including tweets!) and convert them to a one-token-per-row format.
Let’s tokenize our tweets by using this function to split each tweet into a single row to make it easier to analyze and take a look:
ss_tokens <- unnest_tokens(ss_tweets,
output = word,
input = text)
head(relocate(ss_tokens, word))## # A tibble: 6 × 6
## word standards author_id created_at conversation_id id
## <chr> <chr> <chr> <dttm> <chr> <chr>
## 1 catturd2 ccss 1609854356 2021-01-02 00:49:28 1345169706207109120 1345170…
## 2 hmmmm ccss 1609854356 2021-01-02 00:49:28 1345169706207109120 1345170…
## 3 common ccss 1609854356 2021-01-02 00:49:28 1345169706207109120 1345170…
## 4 core ccss 1609854356 2021-01-02 00:49:28 1345169706207109120 1345170…
## 5 math ccss 1609854356 2021-01-02 00:49:28 1345169706207109120 1345170…
## 6 now ccss 1609854356 2021-01-02 00:49:28 1345169706207109120 1345170…There is A LOT to unpack with this function:
- First notice that
unnest_tokens()expects a data frame as the first argument, followed by two column names. - The next argument is an output column name that doesn’t currently exist but will be created as the text is “unnested” into it,
wordin this case). - This is followed by the input column that the text comes from, which we uncreatively named
text. - By default, a token is an individual word or unigram.
- Other columns, such as
author_idandcreated_at, are retained. - All punctuation has been removed.
- Tokens have been changed to lowercase, which makes them easier to compare or combine with other datasets (use the
to_lower = FALSEargument to turn off if desired).
Note: Since {tidytext} follows tidy data principles, we also could have used the %>% operator to pass our data frame to the unnest_tokens() function like so:
ss_tokens <- ss_tweets %>%
unnest_tokens(output = word,
input = text)
ss_tokens## # A tibble: 911,149 × 6
## standards author_id created_at conversation_id id word
## <chr> <chr> <dttm> <chr> <chr> <chr>
## 1 ccss 1609854356 2021-01-02 00:49:28 13451697062071… 1345… catt…
## 2 ccss 1609854356 2021-01-02 00:49:28 13451697062071… 1345… hmmmm
## 3 ccss 1609854356 2021-01-02 00:49:28 13451697062071… 1345… comm…
## 4 ccss 1609854356 2021-01-02 00:49:28 13451697062071… 1345… core
## 5 ccss 1609854356 2021-01-02 00:49:28 13451697062071… 1345… math
## 6 ccss 1609854356 2021-01-02 00:49:28 13451697062071… 1345… now
## 7 ccss 1609854356 2021-01-02 00:49:28 13451697062071… 1345… makes
## 8 ccss 1609854356 2021-01-02 00:49:28 13451697062071… 1345… sense
## 9 ccss 1249594897113513985 2021-01-02 00:40:05 13451533915976… 1345… home…
## 10 ccss 1249594897113513985 2021-01-02 00:40:05 13451533915976… 1345… i
## # … with 911,139 more rowsYour Turn ⤵
The unnest_tokens() function also has a specialized “tweets” tokenizer in the tokens = argument that is very useful for dealing with Twitter text. It retains hashtags and mentions of usernames with the @ symbol as illustrated by our @catturd2 friend who featured prominently in our the first CCSS tweet.
Rewrite the code above (you can check answer below) to include the token argument set to “tweets,” assign to ss_tokens_1, and answer the questions that follow:
ss_tokens <- unnest_tokens(ss_tweets,
token = "tweets",
output = word,
input = text)## Using `to_lower = TRUE` with `token = 'tweets'` may not preserve URLs.head(relocate(ss_tokens, word))## # A tibble: 6 × 6
## word standards author_id created_at conversation_id id
## <chr> <chr> <chr> <dttm> <chr> <chr>
## 1 @catturd2 ccss 1609854356 2021-01-02 00:49:28 1345169706207109120 134517…
## 2 hmmmm ccss 1609854356 2021-01-02 00:49:28 1345169706207109120 134517…
## 3 common ccss 1609854356 2021-01-02 00:49:28 1345169706207109120 134517…
## 4 core ccss 1609854356 2021-01-02 00:49:28 1345169706207109120 134517…
## 5 math ccss 1609854356 2021-01-02 00:49:28 1345169706207109120 134517…
## 6 now ccss 1609854356 2021-01-02 00:49:28 1345169706207109120 134517…How many observations were our original
ss_tweetsdata frame?- 35,233
How many observations are there now? Why the difference?
- 874,214. Each word in every tweet is now it’s own observation (row).
Before we move any further let’s take a quick look at the most common word in our two datasets. To do so, we’ll introduce the easy to use count() function from the {dplyr} package.
Like most functions we’ve introduced, the first argument count() expects is a data frame which we provided with the %>% operator, followed but the column, in our case word, whose values we want to count:
ss_tokens %>%
count(word, sort = TRUE)## # A tibble: 74,234 × 2
## word n
## <chr> <int>
## 1 common 26665
## 2 core 26470
## 3 the 25818
## 4 to 20477
## 5 and 15551
## 6 of 13106
## 7 a 12472
## 8 math 11788
## 9 is 11562
## 10 in 10075
## # … with 74,224 more rowsWell, many of these tweets are clearly about the CCSS and math at least, but beyond that it’s a bit hard to tell because there are so many “stop words” like “the,” “to,” “and,” “in” that don’t carry much meaning by themselves.
Remove Stop Words
Often in text analysis, we will want to remove these stop words if they are not useful for an analysis. The stop_words dataset in the {tidytext} package contains stop words from three lexicons. We can use them all together, as we have here, or filter() to only use one set of stop words if that is more appropriate for a certain analysis.
Let’s take a closer the lexicons and stop words included in each:
View(stop_words)The anti_join Function
In order to remove these stop words, we will use a function called anti_join() that looks for matching values in a specific column from two datasets and returns rows from the original dataset that have no matches like so:
For a good overview of the different dplyr joins see here: https://medium.com/the-codehub/beginners-guide-to-using-joins-in-r-682fc9b1f119.
Now let’s remove stop words that don’t help us learn much about what people are saying about the state standards.
ss_tokens_2 <- anti_join(ss_tokens,
stop_words,
by = "word")
head(ss_tokens_2)## # A tibble: 6 × 6
## standards author_id created_at conversation_id id word
## <chr> <chr> <dttm> <chr> <chr> <chr>
## 1 ccss 1609854356 2021-01-02 00:49:28 1345169706207109120 134517031… @catt…
## 2 ccss 1609854356 2021-01-02 00:49:28 1345169706207109120 134517031… hmmmm
## 3 ccss 1609854356 2021-01-02 00:49:28 1345169706207109120 134517031… common
## 4 ccss 1609854356 2021-01-02 00:49:28 1345169706207109120 134517031… core
## 5 ccss 1609854356 2021-01-02 00:49:28 1345169706207109120 134517031… math
## 6 ccss 1609854356 2021-01-02 00:49:28 1345169706207109120 134517031… makesNotice that we’ve specified the by = argument to look for matching words in the word column for both data sets and remove any rows from the tweet_tokens dataset that match the stop_words dataset. Remember when we first tokenized our dataset I conveniently chose output = word as the column name because it matches the column name word in the stop_words dataset contained in the tidytext package. This makes our call to anti_join()simpler because anti_join() knows to look for the column named word in each dataset. However this wasn’t really necessary since word is the only matching column name in both datasets and it would have matched those columns by default.
Your Turn ⤵
Use the code chunk below to take a quick count of the most common tokens in our ss_tweets_2 data frame to see if the results are a little more meaningful, then answer the questions that follow.
ss_tokens_2 %>%
count(word, sort = TRUE)## # A tibble: 73,595 × 2
## word n
## <chr> <int>
## 1 common 26665
## 2 core 26470
## 3 math 11788
## 4 #ngsschat 3059
## 5 amp 2904
## 6 #ngss 2655
## 7 students 2559
## 8 science 2300
## 9 standards 2273
## 10 education 2174
## # … with 73,585 more rowsYour Turn ⤵
How many unique tokens are in our data tidied text?
- 73,595
How many times does the word “math” occur in our set of tweets?
- 11,788
Custom Stop Words
Notice that the nonsense word “amp” is among our high frequency words as well as some. We can create our own custom stop word list to to weed out any additional words that don’t carry much meaning but skew our data by being so prominent.
Let’s create a custom stop word list by using the simple c() function to combine our words. We can the add a filter to keep rows where words in our word column do NOT ! match words %in% my_stopwords list:
my_stopwords <- c("amp", "=", "+")
ss_tokens_3 <-
ss_tokens_2 %>%
filter(!word %in% my_stopwords)Let’s take a look at our top words again and see if that did the trick:
ss_tokens_3 %>%
count(word, sort = TRUE)## # A tibble: 73,592 × 2
## word n
## <chr> <int>
## 1 common 26665
## 2 core 26470
## 3 math 11788
## 4 #ngsschat 3059
## 5 #ngss 2655
## 6 students 2559
## 7 science 2300
## 8 standards 2273
## 9 education 2174
## 10 school 2154
## # … with 73,582 more rowsMuch better! Note that we could extend this stop word list indefinitely. Feel free to use the code chunk below to try adding more words to our stop list.
Before we move any further, let’s save our tidied tweets as a new data frame for Section 3 and also save it as a .csv file in our data folder:
ss_tidy_tweets <- ss_tokens_3
write_csv(ss_tokens_3, here("unit-3", "data", "ss_tidy_tweets.csv"))3. EXPLORE
Calculating summary statistics, data visualization, and feature engineering (the process of creating new variables from a dataset) are a key part of exploratory data analysis. For our first lab, we’re going to keep things super simple and focus on:
Top Tokens. Since once of our goals is to compare tweets about the NGSS and CSSS standards, we’ll take a look at the top 50 words that appear in each.
Word Clouds. To help illustrate the relative frequency each of these top 50 words occurs, we’ll introduce the {wordclouds2} package for creating interactive word clouds that can be knitted with your HTML doc.
3a. Top Tokens
First, let’s take advantage of the the %>% operator combine some of the functions we’ve used above with the top_n() function from the {dplyr} package. By default, this function is looking for a data frame as the first argument, and then the number of rows to return.
Let’s take a look at the top tokens among the CCSS tweets by filtering our standards by CCSS, counting the number of times each word occurs, and taking the look at the 50 most common words:
ccss_top_tokens <- ss_tidy_tweets %>%
filter(standards == "ccss") %>%
count(word, sort = TRUE) %>%
top_n(50)## Selecting by nccss_top_tokens## # A tibble: 50 × 2
## word n
## <chr> <int>
## 1 common 26599
## 2 core 26405
## 3 math 11688
## 4 education 1917
## 5 kids 1821
## 6 standards 1810
## 7 school 1806
## 8 dont 1622
## 9 grade 1443
## 10 people 1410
## # … with 40 more rowsNot surprisingly, our search terms appear in the top 50 but the word “math” also features prominently among CCSS tweets!
Word Clouds
Word clouds are much maligned and sometimes referred to as the “pie charts of text analysis,” but they can be useful for communicating simple summaries of qualitative data for education practitioners and are intuitive for them to interpret. Also, for better or worse, these are now included as a default visualization for open-ended survey items in online Qualtrics reports and you can even add your own stop words.
The {wordclouds2} package is pretty dead simple tool for generating HTML based interactive word clouds. By default, when you pass a data frame to the wordcloud2() function, it will look for a word column and a column with frequencies or counts, i.e., our column n that we created with the count() function.
Let’s run the wordcloud2() function on our ccss_top_tokens data frame.
wordcloud2(ccss_top_tokens)As you can see, “math” is a pretty common topic when discussing the common core on twitter but words like “core” and “common” – which you can see better if you click the “show in a new window” button or run the code in you console – are not very helpful since those were in our search terms when pulling data from Twitter.
In fact, search terms like these we might want to exclude from a final data product we share with with education partners or in a publication and instead include these these in a title or caption.
ccss_top_tokens %>%
filter(word != "common" & word != "core") %>%
wordcloud2()Your Turn ⤵
In the code chunk below, filter, count and select the top 50 tokens to create a word cloud for the NGSS tweets. A gold star if you can can do it without using the assignment operator or looking at the code above!
ss_tidy_tweets %>%
filter(standards == "ngss",
word != "ngss" & word != "#ngss" & word != "@ngsstweeps") %>%
count(word, sort = TRUE) %>%
top_n(50) %>%
wordcloud2(shape = 'pentagon')## Selecting by nAlso, take a look at the help file for wordclouds2 to see if there might be other ways you could improve the aesthetics of this visualization.
4. MODEL
Now that we have our tweets nice and tidy, we’re almost ready to begin exploring public sentiment (at least for the past week due to Twitter API rate limits) around the CCSS and NGSS standards. For this part of our workflow we introduce two new functions from the tidytext and dplyr packages respectively:
How do you “measure” sentiment?
Sentiment analysis tries to evaluate words for their emotional association. In Text Mining with R: A Tidy Approach, Silge aand Robinson point out that,
One way to analyze the sentiment of a text is to consider the text as a combination of its individual words and the sentiment content of the whole text as the sum of the sentiment content of the individual words.
This isn’t the only way to approach sentiment analysis, but it is an easier entry point into sentiment analysis and you’ll find that is it often-used in publications that utilize sentiment analysis.
The {tidytext} package provides access to several sentiment lexicons, sometimes referred to as dictionaries, based on unigrams, i.e., single words. These lexicons contain many English words and the words are assigned scores for positive/negative sentiment, and also possibly emotions like joy, anger, sadness, and so forth.
The three general-purpose lexicons we’ll focus on are:
AFINNassigns words with a score that runs between -5 and 5, with negative scores indicating negative sentiment and positive scores indicating positive sentiment.bingcategorizes words in a binary fashion into positive and negative categories.nrccategorizes words in a binary fashion (“yes”/“no”) into categories of positive, negative, anger, anticipation, disgust, fear, joy, sadness, surprise, and trust.
Note that if this is your first time using the AFINN and NRC lexicons, you may prompted to download both Respond yes to the prompt by entering “1” and the NRC and AFINN lexicons will download. You’ll only have to do this the first time you use the NRC lexicon.
Let’s take a quick look at each of these lexicons using the get_sentiments() function and assign them to their respective names for later use:
afinn <- get_sentiments("afinn")
afinn## # A tibble: 2,477 × 2
## word value
## <chr> <dbl>
## 1 abandon -2
## 2 abandoned -2
## 3 abandons -2
## 4 abducted -2
## 5 abduction -2
## 6 abductions -2
## 7 abhor -3
## 8 abhorred -3
## 9 abhorrent -3
## 10 abhors -3
## # … with 2,467 more rowsbing <- get_sentiments("bing")
bing## # A tibble: 6,786 × 2
## word sentiment
## <chr> <chr>
## 1 2-faces negative
## 2 abnormal negative
## 3 abolish negative
## 4 abominable negative
## 5 abominably negative
## 6 abominate negative
## 7 abomination negative
## 8 abort negative
## 9 aborted negative
## 10 aborts negative
## # … with 6,776 more rowsnrc <- get_sentiments("nrc")
nrc## # A tibble: 13,875 × 2
## word sentiment
## <chr> <chr>
## 1 abacus trust
## 2 abandon fear
## 3 abandon negative
## 4 abandon sadness
## 5 abandoned anger
## 6 abandoned fear
## 7 abandoned negative
## 8 abandoned sadness
## 9 abandonment anger
## 10 abandonment fear
## # … with 13,865 more rowsAnd just out of curiosity, let’s take a look at the loughran lexicon as well:
loughran <- get_sentiments("loughran")
loughran## # A tibble: 4,150 × 2
## word sentiment
## <chr> <chr>
## 1 abandon negative
## 2 abandoned negative
## 3 abandoning negative
## 4 abandonment negative
## 5 abandonments negative
## 6 abandons negative
## 7 abdicated negative
## 8 abdicates negative
## 9 abdicating negative
## 10 abdication negative
## # … with 4,140 more rowsYour Turn ⤵
How were these sentiment lexicons put together and validated? Hint: take a look at Chapter 2 from Text Mining with R.
- They were constructed via either crowdsourcing (using, for example, Amazon Mechanical Turk) or by the labor of one of the authors, and were validated using some combination of crowdsourcing again, restaurant or movie reviews, or Twitter data.
Come to the Dark Side
As noted in the PERPARE section, the {vader} package is for the Valence Aware Dictionary for sEntiment Reasoning (VADER), a rule-based model for general sentiment analysis of social media text and specifically attuned to measuring sentiment in microblog-like contexts such as Twitter.
The VADER assigns a number of different sentiment measures based on the context of the entire social-media post or in our case a tweet. Ultimately, however, these measures are based on a sentiment lexicon similar to those you just saw above. One benefit of using VADER rather than the approaches described by Silge and Robinson is that we use it with our tweets in their original format and skip the text preprocessing steps demonstrated above.
One drawback to VADER is that it can take a little while to run since it’s computationally intensive. Instead of analyzing tens of thousands of tweets, let’s read in our original ccss-tweets.csv and take instead just a sample of 500 “untidu” CCSS tweets using the sample_n() function:
ccss_sample <- read_csv(here("unit-3", "data", "ccss-tweets.csv")) %>%
sample_n(500)## Warning: One or more parsing issues, see `problems()` for details## Rows: 27230 Columns: 8## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (2): text, source
## dbl (4): author_id, id, conversation_id, in_reply_to_user_id
## lgl (1): possibly_sensitive
## dttm (1): created_at##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.ccss_sample## # A tibble: 500 × 8
## text created_at author_id id conversation_id source
## <chr> <dttm> <dbl> <dbl> <dbl> <chr>
## 1 @LesJohnsonHrv… 2021-01-18 19:53:52 1.24e18 1.35e18 1.35e18 Twitte…
## 2 @Tactical_revi… 2021-02-03 16:44:50 2.35e 8 1.36e18 1.36e18 Twitte…
## 3 @mattyglesias … 2021-01-24 02:09:24 1.67e 8 1.35e18 1.35e18 Twitte…
## 4 @AmandaMGoetz … 2021-01-04 14:30:07 2.27e 8 1.35e18 1.35e18 Twitte…
## 5 @RaheemKassam … 2021-05-05 15:18:43 8.77e17 1.39e18 1.39e18 Twitte…
## 6 @_RedWalrus_ T… 2021-05-02 16:08:51 3.15e 9 1.39e18 1.38e18 Twitte…
## 7 @SelenaCarrion… 2021-04-21 04:12:05 1.31e 7 1.38e18 1.38e18 Twitte…
## 8 #CommonCore #C… 2021-01-24 06:36:06 7.28e17 1.35e18 1.35e18 Tweet …
## 9 @samg399 @Jame… 2021-03-21 18:50:38 1.34e18 1.37e18 1.37e18 Twitte…
## 10 So did we not … 2021-04-29 16:42:37 7.37e17 1.39e18 1.39e18 Twitte…
## # … with 490 more rows, and 2 more variables: possibly_sensitive <lgl>,
## # in_reply_to_user_id <dbl>Note above that we passed our read_csv() output directly to our sample() function rather than saving a new data frame object, passing that to sample_n(), and saving as another data frame object. The power of the %>% pipe!
On to the Dark Side. The {vader} package basically has just one function, vader_df() that does one thing and expects just one column from one frame. He’s very single minded! Let’s give VADER our ccss_sample data frame and include the $ operator to include only the text column containing our tweets.
vader_ccss <- vader_df(ccss_sample$text)## Warning in tolower(wpe[i]) %in% vaderLexicon$V1: input string ':-Þ' cannot be
## translated to UTF-8, is it valid in 'ANSI_X3.4-1968'?## Warning in tolower(wpe[i]) %in% vaderLexicon$V1: input string ':Þ' cannot be
## translated to UTF-8, is it valid in 'ANSI_X3.4-1968'?vader_ccss## text
## 1 @LesJohnsonHrvat @DanteWillBe @MomOf3blessed_1 @gatewaypundit @tan123 He must be doing common core math.
## 2 @Tactical_review That's what common core does to a \U{01f9e0}
## 3 @mattyglesias It’s a can of worms probably best left sealed, but wasn’t this the grand idea behind Common Core? If everyone learns the same skills, corresponding assessments are more equitable, right?
## 4 @AmandaMGoetz I know at work, my co-founder refuses to accept any code commits from my dev team unless they can prove they solved the calculations using common core.
## 5 @RaheemKassam One uses actual math and one uses common core standards.
## 6 @_RedWalrus_ This elicited an emotional response from me at first, which I suppose was intended. After some thought I will stick to my original understanding of why and that is that cursive isn’t used a lot any more and they wanted those hours for other things like CRT and common core.
## 7 @SelenaCarrion @DanHawkins11 @paul_emerich Yea, Common Core strikes me as one more thing that administrators twist around to meet their agendae so *they* can be regulatory and top down.
## 8 #CommonCore #CommonSense Knowledge is power site:Is this #compact style of writing #common or unique as editors say https://t.co/sxW9RHgWzm
## 9 @samg399 @JamesonTucnak @Davethe25219352 @codycr6 @laurenboebert That's some serious common core math right 5here
## 10 So did we not take the same common core classes or did they not pay attention enough to know that any of these can be changed or updated if need/want be https://t.co/Zm1JDivo7V
## 11 A Longitudinal Study of the Human Oropharynx Microbiota Over Time Reveals a Common Core and Significant Variations With Self-Reported Disease https://t.co/zAdkvcMB7o
## 12 @ShilohSheridan @MzmeriABCA Common Core fatality...
## 13 @redsteeze Must be that "New / Common Core Math" they are teaching these days.\nMaybe if we see the sticks, boxes & circles it will make more sense.
## 14 @atlc_a I think the parents could use this lesson :-). Common core......
## 15 Bush or anybody in favor of Common Core.
## 16 @SchmeckleTV @XinminLai @PunishedGPS @CornBardSupreme @legndofphoenix @sofain Common Core says 'conventional order' & lets locals specify BEDMAS(Can, NZ) or PEMDAS(U.S.).\nI was taught Mul & Div treated equally left to right.
## 17 @Rightturn_only @scottfgray @michaelharriot The first time my niece brought home Common Core homework, I just about cried. Most of it was things I had to teach myself because math was never explained to us as kids. We were just supposed to memorize things and if we did one thing different than the book, we were wrong.
## 18 @sxyninjet @TheRightMelissa I am a high school principal. Don't speak on what you don't know. This is a great program. It isn't common core. This is great for all kids. It is real life problems. It is teaching kids to do work. Work in groups.
## 19 The Count and common core math\n #OtherMonstersWhoShouldFight https://t.co/phZcO83LtV
## 20 @amuse Common core educated
## 21 @jnewman1215 Common Core Math.
## 22 @AF632 Common core math failure. 😂
## 23 @CJCousineau @UCPCaucus I won't read his column but I'm guessing it's boomer grandma's react to common core in Facebook comments section good.
## 24 🤡 “New Math”\n\nBiden introduces a replacement for Common Core in his imaginary administration. \n\nBrought to you by Dominion Voting.\nhttps://t.co/ZQiQV1MuND
## 25 #CommonCore #CommonSense Knowledge is power site:Is this #compact style of writing #common or unique as editors say https://t.co/sxW9RHgWzm
## 26 @Sup_Goodies @shreknotbigfoot @Oregonian Well common core is regarded as a failure by an ever-increasing contingent, and isn’t even being used in a lot of places. And state testing is universally opposed by teachers in my district. And teachers (or anyone) self-evaluating is unacceptable and obviously subjective.
## 27 @hues_of_mercury My socialization was totally bungled, but being able to sleep however I wanted was neat. Saxon Math is probably better than Common Core stuff
## 28 @CassyWearsHeels Must be that common core math again, Cassandra!!!
## 29 @DJMessi23 Is that common core math?
## 30 @leslibless Common core math strikes again. Same mathematical process used for counting votes 😉 😂😂😂😂😂😂
## 31 Trump was up to 946,000 vaccines a day, Biden says he’s gonna double it to 1 million, must be that liberal common core math 2+2=5 😂😂😂😂😂
## 32 @hemantmehta I almost laughed when I read his quote about all the (purported) sex and violence, etc., in Common Core, while in the Old Testament these god-ordained things - and far worse - are running rampant from Genesis 1 onward.
## 33 @Azonypse @Frightfur2 @Ace_Archist @POTUS Common core and it's consequences have been a disaster for the human race.
## 34 @Rasmussen_Poll I think they used common core math for that percentage
## 35 Review Online Grade 6 Test Practice for Common Core (Barron's Core Focus) -> https://t.co/CKu3lMDDHL
## 36 @JoeBiden How many is that per state?? #Common core for China Joe...Cmon man
## 37 @catturd2 Common core baby!
## 38 Common core math applied to meteorology https://t.co/bAnqCmhOi1
## 39 Even @QF Qatar Common Core math cannot work out that statistic as factual; nor can the @FBI 's >actual< data on the topic. ~ @TuckerCarlson \n~\nFact Check: Oscar Winner Claims Police Killings Happen ‘Disproportionately‘ to Black People https://t.co/Re7tFcNQAX via @BreitbartNews
## 40 @realgodofhentai @deanlstevenson @AutSciPerson @divinedre11 My elementary age kids are learning common core & it’s just.... 😠
## 41 @parlertakes But they think common core math is stupid...
## 42 @IceColdApathy @csonkaguy @stuwhitney You're an idiot. Common core was a math curriculum. Nothing related to civics, social studies, or political science. Just because you're a communist doesn't mean we should be.
## 43 @RyanAFournier That's actually a math problem from the Common Core curriculum.
## 44 @billinkc 3 - 1 = 0 \n\nIf you have kids in common core math, this is the way
## 45 Download EPUB Common Core Math Workbook, Grade 4: Multiple Choice, Daily Math Practice Grade 4 -> https://t.co/GI5TO5QhIN
## 46 i play jumpstart, but math's hard\nand i can't stand learning anything\n i like when grown-ups call me smart\nbut i'm dumb, so i won't add sums up anymore\nand i barely even care that i'm forgetting all the common core
## 47 @PAKAG2020 Common Core is ONLY English & Math! I assume you suffer from the same problems Reagan found in 1980s & Jay Leno found with "Jay Walking" in 1990s!\nRecent reforms try to help but LOCALS must improve resources and focus on effective instruction.
## 48 PDF Download Prentice Hall Literature: Common Core Edition -> https://t.co/aTcEqpD3Ry
## 49 Idk about the world, but in the US it was the implementation of "No Child Left Behind" and Common Core standards. https://t.co/qsQ0oky52k
## 50 @DAColdriver Common core? https://t.co/iqmXuCC5rO
## 51 @ColorApril @DavidWi69935921 It was the Liberals that, again, had Common Core put into our schools! The implementations of Islam teachings, https://t.co/gexCTEL3xq
## 52 @police_practice We need to communicate more, both academically and informally, between different agencies and different countries. I see very similar issues concerning policing in most western countries. Police culture seems to have a large common core in many places, too.
## 53 @linearlinera kinda sounds like you're using common core on english
## 54 There are a lot of differences in the 6 Orthodox Schools of Hinduism, but here’s what’s common to all \n\n1) Existence of the Atman\n2) Law of Karma\n3) Metempsychosis \n4) Liberation of the Soul\n5) The Vedas as a pramāṇa\n\nThese are the most common core beliefs of what Hinduism is.
## 55 @conspiracyb0t Common Core math + Fluoride
## 56 common core sucks and is totally not of value to a good rounded common sense education , and the math is off the wall stupid https://t.co/tYnAuUErdd
## 57 @AdamLabay @BerksSports @smitty451 @KevinMKruse You're dead wrong and trying to spread falsehoods. Here are literally the Grade 11-12 Literature Common Core standards. https://t.co/TTkgqXwTga\n\nRemember, folks, there are people spreading disinformation to undermine everything. Here's one such person.
## 58 Common core liberal math 101!\nThe more vaccine you make the less people actually get vaccinated sounds about right under a Biden administration! https://t.co/bSx2d2cmPh
## 59 FREE BOOKS were lovely guests. They were just wonderful people and I would have them again. Common Core Math 4 Today, Grade 1: Daily Skill Practice (Common Core 4 Today) by Erin McCarthy.
## 60 That common core math is something else! https://t.co/T7iGBijapJ
## 61 Common Core Skills Training £59.95\nJOIN US ON 11th April 2021 or 17th April 2021 \nTaught by zoom in a small group \n\nMapped to Ofsted requirements \nhttps://t.co/lzWsAtJQdn
## 62 Sounds like Obama's Common Core Math that he pushed out. https://t.co/xEYIFwUWj4
## 63 @rainnwilson ‘Common Core Math Proves the Earth is Flat.’
## 64 @iamcardib This all comes down to education reform. Schools only have so much power when the government is in charge. And people like bill gates who want to meddle with common core, high stakes testing that’s expensive and sucks, etc etc
## 65 Common core math? https://t.co/GZ9z8BuP5v
## 66 @PegGrafwallner @tikaee What is interesting is the overlap of standards. So a state might not call it Common Core but a quick cross walk and the standards match up pretty similar. Same with NGSS and C3 overlaps. Wisconsin SS is interesting with their strands.
## 67 just wrote 3 and a half pages about common core math and that’s not even the longest paper i have to write in this course about math and i HATE IT ALREADY
## 68 Carson Dellosa | Common Core Connections Math Workbook | Grade K, Printable - 👁👁👁 https://t.co/6oepK0EPPS
## 69 @nytimes FYI-\n\nVariants are generally weaker.\n\nE.g.\n(non scientific- non-sense spewing NYT).\n\nNYT= base of the common core education.\n\nTry again: China Times !!
## 70 @WayneDupreeShow @branch77 I’ll be honest with you \n\nThis Microsoft rich guy \n\nOnly got rich by CAPITALISM \n\nNow he wants to murder aka depopulate the globe \nIt’s to crowded in his book \n\nHe dumb down our children with common core math \nTurned them into, confused which body their in \n\nNow vaccines zar ? https://t.co/Dpe9dyNENM
## 71 @ewarren It’s time to open schools and get rid of this ridiculous common core BS you idiotic politicians push
## 72 @iLovelyCoco @brookel1357 @nattikinss @itsLeRaee @montogstardust @whereskaprii @thedigitaldash_ yes because you cant drive and only know common core math but yall talk a gang a shit
## 73 And they complain about Common Core. 🙄 https://t.co/BQkSKxsehT
## 74 @leslibless Yeah, kinda does not add up. Unless you are using common core math.
## 75 @JonMunitz Abolish all private schools and expand and correct the focus of the common core to teach the civil war and African American history correctly as well as to teach financial and technological literacy. Most jobs will go away the kids need to learn to code
## 76 This is giving me flashbacks to all the irrational freakouts about how common core was destroying math education. Spoiler: it didn't.\n\nSome people just love crying wolf https://t.co/jMkvLRfbNo
## 77 @MsJayLee22 lol algebra was okay....I hate math, it’s my 7&12yo’s math that gets irritating common core I think it’s called. I get it that it makes them find all the logical reasons to get the answer but sometimes it’s just Red+Blue=hotdog for real.
## 78 @PennslytckySue @mercerstrains @jordan_fellas The methodology is determined by teachers & textbooks! PPL see classwork but blame standards, like Common Core, without seeing its requirements.
## 79 @KalaniStorey As with Reagan's time, locals & textbooks choose teaching or math methds, not standards like CC.\nThis is what Reagan found in 1980s & what Common Core is trying to help fix!\nhttps://t.co/hqR8cdJb98
## 80 @justcallmephe I mean, not as expensive as you'd think. I think people have this misconception about getting the premium materials and that you must have all the materials immediately. Seriously, let's stop spending school $ on useless Common Core Curr, math tech progs! https://t.co/UpVjo0bpW1
## 81 @TufftedSquirrel @ChoiVGK I like this math!!! Is this the new common core the kids are learning?
## 82 @PeterSchiff It’s still over 50k. Geez common core math has made everyone an idiot
## 83 @DaniRaejax @NicholasFerroni How? Show me - without using common core math - how it makes a difference how many months you get a paycheck as long as it totals the same amount.
## 84 @rweingarten refreshing to hear Delaware Teachers have decided that they will vote to end unions in Delaware. States Governor is Brace man for asking them to consider independent agendas saying fuck common core it sucks and don’t work.
## 85 @T_S_P_O_O_K_Y @JoeBiden So if we have a vaccine shortage, and let millions more across the border, how does that work? I’d love to see the Common Core equation to fix that problem.
## 86 @tbake42 @Cedjy @BryanMittler @JasonSCampbell That country was the United states.bill gates put his good intentions into trying to reform the education system....and it failed ..he admitted it failed...and the only authority he had to try was that he was richer than god.\n\nhttps://t.co/COm4pfxni2
## 87 @RawStory common core math
## 88 #CommonCore #CommonSense Knowledge is power site:Is this #compact style of writing #common or unique as editors say https://t.co/sxW9RHgWzm
## 89 These are common core values shared by many. List three of your core values then learn more at https://t.co/IYahaKkEri https://t.co/hhslZkiwa6
## 90 Check out our online reading and writing summer tutoring programs where we teach students to read complex academic English. #commoncore #english https://t.co/JeFARh7EKX https://t.co/56BpR4Qa0h
## 91 @ToddGloria @JoeBiden @BeKindDreamBig She’s a great pick , we need to get rid of this common core garbage, it’s not working with our kids and makes it harder to help them when parents don’t understand it
## 92 @iJussDoesMe Common core?
## 93 @rexzane1 Isn’t 2+2=5 common core math tho-
## 94 @CCMathResources @Keonyn @PigsBFlying @ACTBrigitte Common core math? Yikes! 😜\nI wouldn't hire you!
## 95 Mobi Free Common Core English Workbook: Grade 5 English >> https://t.co/pzGRcQC2Wy
## 96 @DineshDSouza Is this that common core math? 🤔
## 97 @gdwhat @CrockettForReal Third Grade is rough, we need to re-examine what common core math is really doing to our kids. 🤷♂️😂
## 98 Where are the parents who spoke out against common core? Get set for dangerous Critical Race Theory in every school in America. https://t.co/WryqXX3Ali
## 99 @SweetLittleSins @WesleyLowery The short answer is yes. Every state sets its own state standards for all subjects, except for things governed by other groups, like IB and Advanced Placement.\n\nIn some cases states have joined together to make common standards, for example Common Core in math and language arts.
## 100 @pvtjokerus Agreed. \nCommon and core. Was that meant to be subliminal? #CommonCore is the beginning of the downfall of our educational system. The dumbing down of America. 😥 Everything else you mention falls into place. \U{01f9e1}🇺🇲
## 101 Beware if this Is the same company that partnered with the Gates Foundation and brought us the very very successful Common Core☹️ https://t.co/KMtzxcxdyp
## 102 Common Core Aligned: Classifying Attributes of 3-Dimensional Shapes - IgnitED https://t.co/TQ9N0RTQjL https://t.co/6gJA66Ar32
## 103 Yes along with Democrats common core and 1619 project and plus are kids rank 27th in the world. Are Democrat teachers union have let are kids down. https://t.co/dK6XY7YZi9
## 104 @kdfrox Nah. The Alberta curriculum is whatever they felt like. They don't even teach about dinosaurs now. Alberta *wishes* this was Common Core. \n\nI have a little experience with CC. It's got issues, but most of those complaints are it's too generalized. \n\nhttps://t.co/HrhFaIUSrj
## 105 @CheckmarkLs So sad. Doesn't know history. Common core everybody!!!!
## 106 @kylegriffin1 And push the US into another significant chunk of debt. They must be using Common Core math...
## 107 @Jerny16 @DeAngelisCorey No it's not. The public schools have become a place to control your children and indoctrinate them into radical thinking ideologies. And the Common Core teaching method is horrific and not good for children. Even the smartest children suffer.
## 108 Didn't these demons introduce the disastrous common core??? I hate them with a passion \U{01f928} https://t.co/GMOOKYkdG4
## 109 Big sigh. Biden Taps Ex-Obama Aide Roberto Rodriguez, head of @teachplus & supporter of failed #commoncore & #RTTT for Top @usedgov policy Job. Why or why @drjillbiden? https://t.co/YnozchaB4x
## 110 @AFoxApc @RitaPanahi See common core math and it’s adoption for the answer.
## 111 @historyKehoe @KimStrassel @POTUS If you or a business gets a tax break/tax cut, this is going cost you or the business more $$$?\nIf you don’t have to pay tax on your lunch meal, your meal bill is going to cost you more? \n\n???? Is this common core math or #SenileJoe math????
## 112 @markermarkymark @babysgramma @JackPosobiec @TheJordanRachel I've been saying this for months. I guess it's the common core math they teach now...
## 113 @MaggieforMO I think Common Core did public schools in from our perspective. We observe a lack of analysis, problem solving, and application skills in our teens’ classrooms. The public high schools promote the basic minimum graduation requirements.
## 114 @AdrianaLaGrange Throw it out. You should be embarrassed that it’s a) grossly developmentally inappropriate b) plagiarized from (garbage) American common core & Wikipedia c) culturally and religiously biased d) riddled with errors YOU👏ARE👏A👏LAUGHINGSTOCK👏OF👏A👏GOVERNMENT #FiretheUCP
## 115 @GWillowWilson Try Khan Academy - it uses Common Core teaching methods andI picked it up pretty quickly.
## 116 @JackPosobiec Common Core math. Only way to "vindicate" themselves would be to win their case, but they were 10 votes short.
## 117 Who voted for common core math!? Can we change it?? This is ridiculous
## 118 Rose Webster @GetMyGist here.\n\nTake note #Pediatrics @OakvilleHF @SKAnesthesia @Sunnybrook, #Nuremberg2021 is coming.\n\nCommon core #math by #coroners & #specialists is NOT going to hide dead bodies caused by #CovidVaccines.\n\nPreserve those incomes #oncology & #radiology #Doctors. https://t.co/rupM8LU56T https://t.co/WX2W9XwATz
## 119 This is the new Common Core maths LOL https://t.co/hdfw4LkKMj
## 120 DISTRIBUTIVE PROPERTY OF MULTIPLICATION //3RD GRADE COMMON CORE MATH: Hey Friends, Todays video is breaking down the Distributive Property of Multiplication! We will be breaking down 2 multiplication equations by… https://t.co/xXfwXy7qqi #properties #realestate #phuketproperties https://t.co/On5PvSypXP
## 121 @karol Common core education seems to have this chart looking off 😂
## 122 @Rock64691725 @codycr6 @cathyepage @cuppri_sun @ACLU Is "that dammed common core" how you somehow arrive at Biden being in office for 60 days? 😆😆😆😆
## 123 @MSNBC @kavitapmd So now we have a vaccine with a 74% effectiveness for a virus that already has a 98% survival rate. I’m guessing this is common core math?
## 124 @Michael53915087 I know you're being held back by your Common Core education, so I will put this as simple as possible: The other end, being opposite of where the camera is at.\n\nIt's not aliens, it's just a laser. Please take a seat Mikey, you're done.
## 125 i like when people think i'm smart \nbut i'm dumb, so i won't add sums up anymore \nand i barely even care that i'm forgetting all the common core
## 126 @PatIgo3 @ItsbeenFun2 Common Core Learning is a disaster. What’s your thoughts on it?
## 127 @sarhaJannat @OceanEyes50 @digi11111 @Shannon4Jesus77 @GingerGano Common core.
## 128 @LeppyFN @vnshmax yeah why should they teach you abt the stock market, there should be no common core after like 8th grade. High school you should be able to choose classes that actually reflect your interests and career choices.
## 129 @palan57 What I don't understand is why libertarians who opposed (somewhat justly) NCLB, ESSA and Common Core on the legal basis that the fed govt cannot interfere with school curriculums, are now are OK with the 1776 Commission report?
## 130 "Conservative" values like McCain, Romney, Bush I & II, Ryan, McConnell and the Lincoln Project. Wars, deficits, surveillance and common core. The only reason that libertarianism is gaining traction is because you have no "principles". @stillgray https://t.co/PzxcVhCbb4
## 131 @JunkoSu22993224 Common Core Math, or just retarded?
## 132 @ChristianWalk1r Get rid of the common core system and understand that a calculator can do most of the tedious work \nI'm more concerned if they use the right operations then if they can multiply 3x3 digit #s by place value in 3 different but same ways
## 133 @chukwu_smart1 @engineers_feed In America this would fail because is not done with common core
## 134 @laurenboebert Yeah, that common core idea was the bomb!
## 135 Yeah, this is the standard nonsense from the common core guys. "We're not teaching algebra, but whatever esoteric gobbledegook we're teaching is even harder than algebra" sure https://t.co/GfOzaosnZD
## 136 @LuckeeStrke Common Core Spanish...😂😂
## 137 Why Comics? via @historycomics They are in Common Core. #4CSLA https://t.co/C71cPggQa9
## 138 @WestLinnEagleAH @nypost And let’s just ignore all the Patriots trying to push the infiltrators back and help the officer back up. Nothing at all in the report about the majority of the crowd trying to do the right thing peacefully. \n\nCommon core math skills at its best on what “mostly” means...
## 139 @Kitmcyt @enricooler_two @secretlywinter We actually do learn all of these countries, it’s just common core sucks and only teaches us to memorize stuff long enough to take a quiz. I can name all the countries on this map lol
## 140 @MSNBC 100 million over 100 days is 1 million per day, correct?\nEven with common core math?\nUnder Trump that was already achieved. https://t.co/XnWuZBCs7U
## 141 @RealMattCouch Common Core is an excuse for STUPIDITY!
## 142 @QuarantinedCoof So what your saying is only big racists dislike anything common core does 🤔
## 143 #CommonCore #CommonSense Knowledge is power site:Is this #compact style of writing #common or unique as editors say https://t.co/sxW9RHgWzm
## 144 @C_Deskie @GGlocksX @scottydog57 That and common core.....
## 145 @4HumanUnity @seanhannity IT'S THE TRUMAN SHOW, SILLY. \n\nNOT ONLY WERE THE ENTIRE CLUSTER OF CRIMINALS, RE-ENSCONCED ..\n\n(MOMENTARILY)\n\nBUT YOU GET TO PAY ...THEM. ..MORE ...\n\nAS A TOKEN, FOR:\n\nLOL\n\nSUPPORTING CRAFTY, ILLEGAL VOTES & WEAK TWISTED....SISTERS\n\nI SMELL, COMMON CORE.\n\nhttps://t.co/h4nMD53nx1
## 146 @DineshDSouza Of course they do, their followers still think 1 + 1 = .25 cause common core says so.
## 147 That’s why I took my daughter out of school.While everyone is screaming,Open the schools..they are still forcing masks and making them run and do laps at recess for https://t.co/QU9oLePXXd daughter couldn’t breath-7 hours a https://t.co/3ffzLNj5S8 indoctrination-Race/Common Core! https://t.co/aMRNBL4jTz
## 148 @misplacedcomma2 Is it common core? That stuff gives me a headache.
## 149 @robbysoave When CA rolled out Common Core they did exactly the same thing ‘this new technique will make everyone equal at math thus there should be no accelerated classes’. It wasn’t mandatory, but multiple school districts got rid of accelerated math.
## 150 @AreInmates @realchrisrufo All of the above, with common core math as a foundation and zero reading skills or reasoning abilities, todays students are what we see rioting in the streets today, for the past 20 years this is what our schools have been producing \U{01f972}
## 151 @Davidtmp @curtis08467059 @AZGOP @kelliwardaz When someone gets more votes, they win. That’s not common core. That’s common sense. Biden got more electoral votes therefore he won. Get over it, Trump lost.
## 152 @WINDOCTORRX The left’s common core math.\nJust like their genders
## 153 #CommonCore #CommonSense Knowledge is power site:Is this #compact style of writing #common or unique as editors say https://t.co/sxW9RHgWzm
## 154 @GayLatina4Trump Common Core math would never lead to putting humans on the moon. I think Common Core dumbs every student down.
## 155 @SusanStJames3_ Common core math.
## 156 I vividly remember Common Core, also courtesy of Gates. It’s almost as if he’s trying to destroy education for successive cohorts of students by swinging between wildly different paradigms. Or maybe he’s just a charlatan. https://t.co/dXAgNL7aip
## 157 Yeah, the public school I’m in has more fleshed & funded curriculums than the charters I was at. Charters & privates are not bound to common core so they can make up whatever they want & teach it to your child. \n\nI know because I use to do it. That’s literally not it. https://t.co/ro3MIoSChi
## 158 Check out Prentice Hall Mathematics Course 1 Common Core Teacher's Edtion 2013 Edition... https://t.co/N4K1cE03kk via @eBay
## 159 @EuRollout How TF you gonna vaccinate 112% of 100%?!? Dammit this common core math is so racist! 🙄🤡💩
## 160 Together, these hours of curriculum and instruction are comprehensive and integrated, meaning they explicitly teach strands and standards of the Common Core English language arts (ELA) standards for each grade level.
## 161 This is very imminent right now and it’s happening in Republican States as well.With the indoctrination-Common Core and Medicaid Psychologists in our schools.Everyone wants schools open immediately,but there have been no changes in these programs whatsoever.Bullying is rampant. https://t.co/12hnI9gO3t
## 162 @veteranhank Brought to you by the folks behind “Common Core”
## 163 @MishaOBrien18 @RealPatriot56 Sadly we probably can't reclaim it. Clinton began the liberal miseducation of our young people and it's become worse with each passing year since. Then Common Core convinced many children that their parents knew nothing worth knowing. It seems almost like a deliberate plan.
## 164 @sepdeironarrow1 @alicekeeler @youcubed Do you know YouCubed is designed for California & Common Core standards By a CC promoter?
## 165 @shOoObz It showed too when they tried to implement common core. My straight Ds having ass helping kids with straight As pass tests cause school stopped being about memorization and regurgitating information.
## 166 @TheyCallMeTomO1 @krugster27 Nope. Unfortunately, sarcasm was left off the common core curriculum. 😏
## 167 The dumbing down of American students using common core and propaganda means they have no idea of the meaning of Fascism nor the historical results of regimes governed that way https://t.co/Flv0uyOAb6
## 168 @GholdyM: Who wrote the common core standards? Who wrote the curriculums you're using? Who created the assessments? What do those people look like?
## 169 @offgridlife @VioletHemingway @royalpratt @davidaxelrod @chicagotribune Honest! I blame common core math 😆
## 170 @SceneAlot @melindagates @BillGates They gave the Memphis school district 100 million dollars...social experiments. Destroyed the district, then walked away. BTW. The Common Core Curriculum was developed and funded by the Gates Foundation
## 171 Regardless of what may, or may not be, a part of the common core curriculum, I'm here to remind everyone that Americans kill Nazi's. It's kind of what we're famous for.
## 172 @alxcharlesdukes @IDoTheThinking Not sure how you’re defining algebra but kids learn about variables representing unknowns in common core elementary school and are solving equations in middle school. I think they learn it early enough but we rush so many things that very little actually master it.
## 173 @MsSamanthaMay Got more out of cursive than common core math 🤣🤣
## 174 @flasportsbuzz Common Core math
## 175 @matildaaep Ah yes the common core standards for teaching secondary social studies, my true source of happiness https://t.co/cQ1uZPYp2B
## 176 @us_biden @pablo_honey1 @Jim_Jordan I agree we could do without you common core, cnn watching ftards\nAnd your scamdemic to fill the pockets of the drug companies
## 177 @SparkyPatriot I say it started in the 60s with all those damn hippies, then the 70s when China got involved in American gov. and then Obama teaching the Common Core bs and just the absolute stupid stuff that’s blasted on SM, & now have all these movements for rights they think they don’t have.
## 178 @paxcyclist So, Common Core Math is working over in 🇬🇧...😂 Ed, look at the bright side of things...your not mistaken for RoseMary or the third wife....LOL.
## 179 @cathponeill It's that great democrat common core math... only they understand it and it never works...
## 180 @georgejcip I think you needed to have been taught with common core math to figure it out
## 181 Just agreed with a right wing friend of mine that it was the Democrats who passed common core maths.\n\nIt's just that he meant legally, and I meant academically.
## 182 Read Free Common Core Achieve, GED Exercise Book Social Studies (Ccss for Adult Ed) >> https://t.co/VcAL9S2EK7
## 183 @_danigilbert @CaliConserv1 common core made it happen, thanks to hussein obama.
## 184 @SlayLikeAGirl @LaBeautyologist @divinedre11 That common core right there
## 185 @thommanster @GovernorTomWolf Common Core is horrible.
## 186 @amuse Yeeeeeaaahh, No.\n\nHere's a better "classroom concept" for y'all.\nConnect with me and I will help you with the curricula.\n\nSpoiler Alert: It will not include The State or Common Core.\n#homeschooling #homeschool #HomeSchoolHub @DrTiaJolie https://t.co/gXfYi1wbIr
## 187 Common core meet woke core? Virginia moves to end all advanced math classes before 11th grade as part of ‘equity-focused plan’ https://t.co/aIrzalEzMM via @twitchyteam
## 188 @BrandonStraka @SharylAttkisson Common Core Math, obviously!🇺🇸🇺🇸
## 189 Wow, as if we didn’t destroy math education enough with Common Core, now we are going to burn the ashes if this is true. Glad I had my kids take their math at the JC instead of my school. https://t.co/vIKukwSsts
## 190 @matticareforall @SarahConnor39 Totally agree. Same here. I was initially against common core math but after helping the kids for years: a lot of it makes sense. I make sure my kids know the “old fashioned way” as well, whether it is taught in school or not.
## 191 @imasurvivor66 @JayC1l Keep Home Schooling - what Illinois PUBLIC schools teach YOUR CHILD: (Common Core)\n2nd grade: My 2 Dads\n4th grade: A Green Christmas\n5th grade: Advantages of Anal Sex\n7th & 8th grade: 1 month on "What it's like to be Muslim."\n1st – 8th: 1month Black History + 1619 PROJECT https://t.co/iQctBkpLZ2
## 192 @BGavathas @Sully___77 @GeorgePapa19 Yes 81 million all from his basement. You must teach that common core math 😂😂
## 193 This #BlackHistoryMonth, teaching will be a bit easier. \n\nVisit https://t.co/lb5gvM1ooL and find adaptable curriculum for K-12 education "Mary Church Terrell & Her Quest for Social Justice." \n\nThese lessons are fully Common Core aligned & complete with activities for every level. https://t.co/TJ4CcOjqs7
## 194 Lol. This is the logical result of common core math https://t.co/KnJWoffbuC
## 195 @TheTristenBrown @Breaking911 They used common core maff.
## 196 @jerry6667 @Tex_968 @vjeannek @ScottTBrower1 If you say something is off and use common core as your throw away line I'm going to discount everything else you have to say.\n\nPut up your data and source with a bit of courtesy and I will treat you accordingly.
## 197 @kamatsu8 @el_anecdoche @MyGardenLady @stephstephking We can absolutely talk about methodology if you REALLY want to go down the Common Core wormhole, which is deep and convoluted and has resulted in incredible and lasting harm to US education IMO...but when Bill Gates has the ear of the EdSec that’s what happens. 😢
## 198 @ScottBaio omg ugh My brother moved his kids out of 2 schools because of common core 👎👎
## 199 Common core? https://t.co/Uk5Pexgtji
## 200 @adamcarolla They never have. Neither do the politicians that implemented common core math.
## 201 @XF_Nova @ESPNNBA He is using common core!
## 202 @gatewaypundit @GayConservativ3 Common core math with a Dominion algorithm.
## 203 @WINDOCTORRX @reg1776 You have to understand \n“ common core “, the destruction of American education https://t.co/9TCICHyHsO
## 204 @BrandonStraka Common Core.
## 205 @tinfoilmask @Breaking911 Common core was too hard, even. Sad!
## 206 @Badams820 @LadyOfTheOcean1 @RickySi16087724 @_Kel_Varnsen_ @4ever_patriot @medwoman1 @giddy_bunny @enufs_enuf @1Quetzalcoatl1 @RobfromMO @mathmomma24 @MrChingonE @JosephH27418496 @PaulStetson13 @epitomeof3 @ziggystardad @schmevil @JDW714 @RAGINxCAJUN @Emma34770971 @MistressRedWasp @ahrehead @PoliticsPot @jdd2169 @laylow88861429 @henriziolkowski @annieka77 @StrokeyStratfo1 @wknu_radio @CaptainTeag @JeffreySHarper1 @RICHARD53463775 @22CB22CB @Vickie627 @Forseti_Pazzo @JonSmith922 @QuidRises @Merry_Hippie @ConcernedinPV @Ihonestlydont19 @BSHerrle @waywardmegan15 @PATRIQT_1776 @MsDianaFrances @Sinner_Lilith @TeekeeMon @ruxcytbl @Lastplace_champ @thedemorats @skmvy5 Common core math or some such...
## 207 @UTLAnow @adamcarolla Bullsh!t. Unions and many teachers are working for the socialist(s) in the White House. Unions haven't work in the interest of teachers or students in decades and teachers haven't stood up for children or they'd be damn sure we got rid of Common Core.
## 208 Standardized testing and common core were instituted to mask declining education rates\n\nThe same way fire and police departments lowered their physical test standards to obtain more equality in the ranks
## 209 I got one math question wrong working with one of my students and he had the nerve to say “I’m smarter then you” boy Idk that common core
## 210 @jsolomonReports Did they use common core math? Or is this based on political science?
## 211 @citizenstewart Yes! I hope this pandemic will get states to switch to structured literacy and get rid of common core math.\nhttps://t.co/UzFB4Iyi8j
## 212 Can't say I'm surprised that social studies standards have provoked debate in Minnesota having followed the common core standards a decade ago. The discussion is ongoing for the standards committee in MN & people who want to give feedback. Backstory here: https://t.co/idYWJtcLdq
## 213 @masterfulcoach ALL humans with a "normal" brain have the aptitude & capability to learn math, science & most importantly LOGIC. Common Core is the devil & is NOT logical. I was born in 1978 so luckily I evaded that nonsense. ANYONE can #STEM. Rocket science is actually fairly easy to understand
## 214 @THEK0UNT Common core curriculum taught me what foreshadowing is.. AND THIS IS IT
## 215 I have gained over 20 new followers the last two days yet the number of people following me has gone down. Is Twitter using common core math? Just asking. It's all good 😊
## 216 @mikewhelanjr That common core math in action right there
## 217 So I've lost about 30 followers now and can't even check the shadowban checker cause it won't work 😕 stupid Twitter you're using common core again 🙄
## 218 @Cheese12987 Outcome based education arrived in the 90s then Bill Gates took over the system with Obama's blessings and forced the country to embrace the totally dumbed down, do what your told system of Common Core. Gates knew exactly when Idiocracy needed to kick in.
## 219 @donkamion78 Can never trust the guy who came up with Common Core math. Ruined generations understanding math.
## 220 @RobSchneider He has no education degree yet he was involved in Common Core, he has no medical degree yet he’s involved in pharmaceuticals, he has no agricultural degree yet he’s involved in farming. How does this happen? And now we find out he’s an adulterer. No morals. No ethics.
## 221 @stephen_cm89 @Ace31841 @mcsoaz Common Core math isnt about being right, just feeling good they got to participate.
## 222 @001Mellie @ekimtnek1 Damn common core math!!!! I’m as confused as he is
## 223 @spillygoat19 Common Core Math
## 224 So ive read but not researched what im about to say.. common core education a ccp program to dummy down our future generations ..while i sit her pondering my out rage why my 12 year olds never learned about the american revolution and watch these probably high schoolers
## 225 5. Students in SHS1 shall continue to run the common core programme for one year thus students into SHS1 shall not select science, business or arts programmes.\n\n6. At the end of SHS1, Students shall write a Common Core Exams into SHS 2.
## 226 @rickhess99 @carolinecdamon @tomloveless99 #Commoncore wasn't a "nothing burger" b/c it was actually harmful in driving out real content as well as literature from curricula in favor of boring & disengaging BS like "close reading" - w/no evidence behind it. Thus I disagree w/both you @rickhess99 & the critique you posted.
## 227 @raynor_sebas @NC_Governor But you have a 99%+ recovery rate for the virus. Common core mail fail.🤦🏻♀️
## 228 Schools Test-Drive Common Core - https://t.co/7IWbQMdDMY
## 229 @bethroessler @CNBC @kaylatausche Standards like Common Core are content requests intended to give educators some control over textbooks.\nStandards were recommended in 1980s because locals traditionally depended on publishers to define curricula.
## 230 @Fights_For_Kids Take a look - it actually is based on common core which funny enough originated in the Us then went abroad and now is back to the IS. https://t.co/95ng7QxLY6
## 231 Huntington programs develop the skills, confidence, and motivation to help students of all levels succeed and meet the needs of Common Core State Standards. Call 703-880-8300 to get started today! https://t.co/YTruya9Dfr
## 232 You know how there are parents that say “COMMON CORE! BOO! LET ME TEACH YOU TO DO PROPER SUBTRACTION” I feel like that when I make my kid use a metronome and count “one and two and three four” instead of running running walk walk or whatever.
## 233 @azureblue68 Yes,if working with children.I don’t think people realize the Medicaid psychologists in our schools &what they’re doing.This was all under Obamacare and Common Core.Bullying is so bad& schools aren’t handling it and they’re not being taught correctly.They end up in counseling. https://t.co/HS4Eyfm8kY
## 234 I don't know how much of that was just in the teacher conference/union pedagogy zeitgeist at the time and how much was actual curriculum, so I don't know how much in either year it stretched (but almost certainly stopped at common core)
## 235 @bob__miles I’m scared of them, but only because one of my big selling points is being decent at math, and they all grew up with the far superior common core math curriculum
## 236 @SugarMAGA Factor in gas prices and that $1,400 will net to $0 by years end. Are these people common core math grads?
## 237 @WolfGames2021 @PennslytckySue Bill Gates and #CommonCore.
## 238 @Crumplepoint Literally Common Core 100%
## 239 @arneduncan We don't even show ID to vote. Wth are you talking about? I thought you disappeared. I will never forgive you for the Common Core disaster.
## 240 @inversebrah Common core math teacher can explain this
## 241 @leezeldin PLEASE PLEASE SAVE US FROM CUOMO!!!! WE ARE DESPERATE!!! LOOSING OUR BUSINESS,KIDS EDUCATION (OUT WITH COMMON CORE, IN WITH HISTORY AND REAL LEARNING) PLEASE IM BEGGING ON BEHALF OF MOST NEW YORKERS!!!!!!!!! DESPERATE PLEA!!!!!!!!!!
## 242 @SargeantBee @notyourenemy7 @AyannaPressley I'll explain how it works to you community college get your common core classes done that cost you about 7 grand which you earned working and going to school then and here is the part your not getting you transfer those classes into whatever state school you chose and commute
## 243 @K_M_H34192804 Your math is wrong @K_M_H34192804 , they are using Common Core math and under common core math any combination of numbers you deem add up, then it is right no matter what.
## 244 @SDunaway6 @DonaldJTrumpJr Killed more than Hitler???\nJesus is that common core math you're using? Explains the stupidity of your answer!
## 245 @Rockymtngirl25 I don’t remember much from algebra but I do remember you always solve the problem in the parentheses first, then complete the remaining math, and the rest of the bullshit to get the answer. Well PRE COMMON CORE
## 246 What are the process standards/mathematical practices and how do we incorporate them into all of our math instruction? https://t.co/OzO77SLvFA https://t.co/yHeORsLoQp
## 247 @glg1101 @cpa_kay @gburton @azcentral We are in trouble if they believe in Common Core Math that 2+2=5... serious trouble! \nHahahahaha
## 248 .@washingtonpost columnist Jay Mathews on why #CommonCore was such a colossal failure: https://t.co/QKxDtwIYSn #education
## 249 @livingbreadgurl @Me96647320 Thanks #commoncore. https://t.co/H5bbVhPtsJ
## 250 @SimplyNi That's that common core
## 251 @LayahHeilpern @brianjoralvarez Its that common core math that they teach the kids now...
## 252 @RepKinzinger Is that common core math you’re using Adam? How much has CNN been paying you to kiss their ass?? How does it taste? \n\nMy 10yr old knows that you’re only allowed ONE vote and dead people do NOT count. \n\nTake the rose colored glasses off.
## 253 @MsJR88 This has to be that “common core” think-outside-of-the-box math that they are pushing nowadays.
## 254 @TomHamill2 Fuck Common Core
## 255 @scolacino @WDCreports They just need to acknowledge Trump making it ok to say Merry Christmas, say all lives matter as long as they stay in their own damn bathrooms, then reference socialism, common core math, Baby Jesus, so called EXPERTS on the left, and “the plan.” It will be totally believable.
## 256 Why I Am Joining The Jan. 6 DC March For #Trump https://t.co/TsPp1cg1dE #SchoolsAreNotSafe, #Lying #Legislators, #CorporateCulture, #LyingMedia #CommonCore #ElectionInterference #tcot #ccot #pjnet #TeaParty #TeaPartyForum #TeaPartiesUnited #TheFacebookTeaParty #WalkAwayCampaign
## 257 @jmbenson1491 they don't get it at all...common core stole their common sense. they think the collective works better than the individual and hope they will be taken care of. oh, they'll be taken care of alright, not in the ways they hope tho. SAD.
## 258 The only bad union is a cop union. The problem with schools is that funding is usually tied to property taxes and that a bunch of ideological billionaires keep pulling dumb shit like Common Core, it's not that teachers have job protection.
## 259 Remember how Gill Bates was one of the chief architects & promoters of #CommonCore so that he could create a massive market for Microsoft to exploit? At you & your children's expense, of course. https://t.co/EMVsbW2L8Q
## 260 The man behind common core repeatedly gave interviews that it was created to combat "white privilege" and though it started out small it spread nationally. Give it a year and this New York program will be in found from Missouri to Vermont. https://t.co/fozzu8wDA3
## 261 Please get familiar with educational rights....\nGrants that demand schools teach curricula like Critical Race Theory (CRT), or common core are a violation of the law. U.S. Code – Unannotated Title 20 Education § 1232a. Prohibition against Federal control of education.
## 262 @Tony__Heller @MarchForTruth30 I take it common core math doesn't work with advanced scientific principal.
## 263 @WINDOCTORRX That is the new Common Core Math from the virtual teachers.
## 264 @JordanSchachtel The stupidity of not realizing how we’ve survived all these years off our immune system is astounding. We used to raise our kids to be leaders not followers, to use common sense not common core:( smfh
## 265 @SpookdBlog Janet Yellen is to old to have had Common Core. Must be traditional ed that produced generations of mindless math illiterates with U.S. ed laughed at by other countries.
## 266 What do you notice and wonder? #noticeandwonder #mathed #elemmathchat #FractChat \nCommon Core State Standards\n#3NF1 #4NF2 #5NF1 #ccss https://t.co/RLV69H0A1m
## 267 @brianjoralvarez Is that common core math?
## 268 @MAGA94365921 @FANofUSA @catturd2 Interesting how the same publisher who produced common core and published the text books under Obama also was the publisher of his book!
## 269 More than 1/3. They must be using #CommonCore\n\nJoe Biden's 'President Harris' Gaffe Comes after Third of Voters Doubt Mental Fitness https://t.co/v0YtwBIDpW
## 270 @Malkuth1974 @DebDebInCT @therecount Yeah... Because those people would totally vote Democratic if not for the gun issue alone... They're all good w/neutral bathrooms and common core, but the guns is the issue....
## 271 Tyrus equating @tedcruz to Maxine Waters did it for me. What a liberal dumbass. He used to be funny. Now he is just a common core leftwit. The show would be better without him. https://t.co/pDYHj4c2bq
## 272 DOING MENTAL ARITHMETIC (aka COMPUTING) is NOT a good idea. I tried it (back in the day) and it was WORSE than anything in the COMMON CORE. I “multiplied” some factors mentally and woke up 4 days later still on Spring Break. With no idea of how to identify primes. DON’T DO #MATH. https://t.co/wUqbebcNW8
## 273 @ResisterChic @GeorgePapa19 Stop doing meth. Do common core.
## 274 This isn't common core. https://t.co/0luJUOBqBN
## 275 Critical thinking skills are not part of the common core curriculum https://t.co/PvuLReo6hT
## 276 The Ghana Education Service (GES) has announced that it has indefinitely suspended the implementation of the Common Core Program (CCP) for Junior High Schools (JHS) teachers following the action by participants. https://t.co/63kvYHaDta https://t.co/ksqwgZ6oNY
## 277 @leoniehaimson @ksprowal @UFT Same guy that hissed and spit about how teachers wanted to protect their common core standards?
## 278 Common core math apparently. https://t.co/yDXOfJVUlN
## 279 FREE RESOURCE | Help Brick and Oz tackle frequently confused letters, like b and d! --> https://t.co/Kj4iYNsv9E\n\nCommon Core alignment: CCSS.ELA-LITERACY.RF.K.1.D.\n\n❤️ Be every child's most loved English teacher!\n\n#ELAteachers #teachertwitter https://t.co/uQRhsvpwPD
## 280 @LoriLightfoot you sound so foolish. Take responsibility for your mess the situation going on in your city happen long before Trump more like during Obama and common core he pushed. No one believes your crap. https://t.co/YaqDaTcYSE
## 281 @FDS2wx I wish we had them back in school, instead of this crazy Common Core!!
## 282 @dunnmike84 Who told you Common Core math is stupid?
## 283 @TitaniaMcGrath This is common core math with more steps....
## 284 Download Free 5th Grade Common Core Math: Daily Practice Workbook - Part I: Multiple Choice | 1000+ Practice Questions and Video Explanations | Argo Brothers => https://t.co/ShFBlgPTTA
## 285 @WednesFri @BasedSavannah @JackPosobiec @DonaldJTrumpJr So you believe the POC who were denied GI Bill benefits are all farmers today?\nHoly cow! (Pun intended)\nYou are flat out the wisest person ever to live in the USA.\n\nDon't tell me, they taught CRT in your high school - right?\nAnd common core math?\nAnd the 1619 project?
## 286 @lgadbery This explains why they're sending our money for gender studies in the middle east. When they try to put the pieces back together they can just Common Core it and claim it for inclusive purposes.
## 287 Common sense not common core https://t.co/jLZUW009Mo
## 288 I should have gone to school to be an epidemiologist. Getting paid to never be right sounds pretty cush. It’s like if common core was a career field.
## 289 Access EPUB:\n➤ https://t.co/EKmRb7wtTI\nLooking for 【Pathways to the Common Core: Accelerating Achievement】 [Download] Mobi\n▶ by Lucy Calkins
## 290 I’m sure they’ll make provisions for the control group. \n\nWait you say...? There is no official control group? \n\nThat must be Common Core Orthodox Science, because I’ve never heard of such a thing. https://t.co/1jkoNLTjDP
## 291 Is this the new math? No wonder Obama pushed "common core" https://t.co/dKr7Tnog3Z
## 292 @Breaking911 My rent is 1100 per month, they must be using common core math
## 293 @WTF_OS @StanTradingMan All bashers use Common core math, thus it will take them longer to figure it out!\U{01f92a}
## 294 @PhillyWCWagon @KatiePavlich @disneyplus @ginacarano How the hell is that anti Semitic? That’s just pointing out history and saying how Jews were mistreated. I can’t even if that how your math works. You must be using common core to calculate that.
## 295 @designinginward @cydharrell @divinedre11 Yeah, it's how it works in my head, too. First time I saw the Common Core math I went "oh, right, that's how I already do it."
## 296 I showed some friends some of the evil common core math and everyone started yelling about how much easier it was https://t.co/sR84oNO5co
## 297 @MikePerlberg I don’t think of this as a cycle so much as a continuum. Began with Nation at Risk, Goals 2000, NCLB, Common Core...all efforts at reducing education to an algorithm that could be executed by ‘teacher proof’ charter schools or computers.
## 298 @SaraGonzalesTX Yes they can. Between indoctrination and common core, these people are GENIUSES
## 299 Nostalgic for when common core was a culture war issue
## 300 @jbendery This must be that Common Core math........
## 301 * Opportunity Zones are expected to spur $100 billion in long-term private capital investment in economically distressed communities across the country.\n\n* Trump directed the Education Secretary to end Common Core.👀👀\n\n* Trump signed the 9/11 Victims Compensation Fund into law.
## 302 @PqmVic @kayleighmcenany No. None of us buy this common core science you want to shove down our throats. True science can be replicated over and over with the same results. Haven’t seen any to support this fear mongering. But I have seen plenty to dispute it 😉 https://t.co/UQYCFCK4Ll
## 303 @realstewpeters Please help end public schools discussing gender changes (without parental consent) to very young children. 😱 Run from M A L 0 V € N T public schools...common core, hate, non minority hate. This isn’t education, it’s destruction.
## 304 @BostonMMAGuy @projectksl @PFF No. Its 11. You are 100 percent wrong here. You have to count 15 if counting up to 4. I know common core math sucks
## 305 @John08248182 @stillgray Right? \nMust be using that Common Core Math. \n10 frame + 4 counters = Purple...? 🤷🏻♀️😂
## 306 Hey moderates, did you know DeSantis has done the following?:\n\n1. Significant raises for public school teachers, and got rid of Common Core.\n2. Got rid of Scott’s law which prohibited MMJ patients from buying flower. \n3. Support for conservation (land and water).
## 307 Common Core was the worst thing to happen to the public school system in the US https://t.co/XCd0Zg6sEE
## 308 @FCEA_KY People are waking up to the lie that is public education. And you know what pushed us over the edge? The indoctrination! \n\nThis is the beginning of the end for the cookie-cutter, Common-Core-to-dumb-our-kids-down DOE agenda. I know, I know. The CCP & Soros aren't happy with you.
## 309 Continuing series: Famous tropes uttered by school administration:\n\n"The reason you don't like Common Core math is because it wasn't the way you were taught."\n\n@manateespirit @StopFedEd @erickalenze @sandstorms @DexTeacher @KansasisinCalif @rcraigen
## 310 @BoSnerdley Common Core math—I do not believe that number anymore than I believe Joe got the votes to win the election. I give credit to American people and not who is conducting polls
## 311 @0H__I0 @Breaking911 There is a difference between counting and AUDITING. \nUnless you studied COMMON CORE ???
## 312 @breannamorello Common core math. That's how.
## 313 @DialectsIreland @danielmcauley @AdamCSchembri @sj2915 @RobDrummond @CorriganKaren @BrilhanteAnthro @katieravenna I’m not a sociolinguist: I have been compiling glossaries of common-core youth vernacular and of UK street slang usages relating to criminality and Drill lyrics. I hope I’ll be able to make these available later this year.
## 314 @Scumdog666420 I can’t help my daughter with her math stuff because common core math is stupid 😂😂😂
## 315 @itsjustme3434 Now if Common Core had this in it...
## 316 Download Kindle 4th Grade Common Core Math: Daily Practice Workbook - Part II: Free Response | 1000+ Practice Questions and Video Explanations | Argo Brothers => https://latestpopularhighlight.blogspot.combook52.php?asin=1946755478
## 317 @ABenMcCloskey @ShayMcAlisterTV Common core
## 318 I love using @Classkick to reward students with common core growth mindset math stickers and NGSS I CAN Stickers in guided remote lessons. https://t.co/fvg2tfvlDL
## 319 @misplacedcomma2 Ah! My son loved math. I was just..average. Common core just makes no sense but he flourished .
## 320 See?\nIgnorance is not bliss\nit is just ignorant\n\nHere is a little common core math for waitresses that are dumb\n\nIf Johnny's food bill comes to $600\nand his bar bill comes to $1400\nHe owes $2000\n$2000 means $2000 - not $2600\n\nAOC = 🦇💩\U{01f92a}🤡 https://t.co/WFxt06ccqY
## 321 @Dviddles @RepMattGaetz Payoff to George Soros through Common Core contracts with Pearson Publishing
## 322 @thenation Public school teachers have known about Gates since he paid for the Common Core Standards, hated by teachers and ignored by media. Speaking of ignoring schools, take a look at what the DOE is up to these days:\nhttps://t.co/4ysfS7tmof
## 323 @VivekGRamaswamy math is only racist, when it is common core math; telling us that 2 + 2 = a little red wagon pulled by a black girl. racism is a trap of destruction, for all .
## 324 When Common Core arrived, the Catholic Schools of Sioux Falls chose to go in a different direction. https://t.co/5YvbfwrPQT
## 325 @daphnehk @cyberleagle @jilliancyork @thepublicdomain Yes and yet still both are a development of the enlightenment and a normative notion based on rationality, universalism and essentialism. So there is common core and that essence is very much palpable in the way how the two work normatively
## 326 Terrence I think it’s called common core math and now we know just how common it is!!!!! https://t.co/lIrOZLqVzO
## 327 @aussiegirl3333 Common core math...well done Department of Education!
## 328 @sammons_jacob @serbobross common core math really fucked these people up
## 329 @CoriBush $600 + $1400 = $2000 (of course you can't use common core to calculate it.)
## 330 @contradiction70 LOL! \n\nBut I don't think they even know what those are anymore. I think Common Core got rid of all that "straight forward, makes sense" stuff ;-)
## 331 @catturd2 No. Obama common core math. Control the teachers. Control the students. Control the future. Classic Nazi tactics.
## 332 @joaktree33 Yes. Also, beginning with language and then the implementation of common core education/standardized testing further dumbs down intellect, stifles creativity and (shudder) has potential to socially engineer
## 333 @Breaking911 @lynnethill I’ll answer my own question. The reason minority enrollment is low in honors/gifted math and sciences is because of a dummied down curriculum called “Common Core”!\n\nFlorida threw it out and their students are excelling!\n\nDedicated teachers teach higher level thinking skills!
## 334 @timburchett Common Core Math 🤦
## 335 @marjieros @thetoyman1 That's what common core has done.
## 336 @AOC @ScottAdamsSays Hey AOC! I live in Denmark and those “high school” kids has to pay 48% taxation do your common core math! And guess what? they’re “students” not the head of “household”! 6 weeks paid vacation? they need to pay them selves money from their paycheck into government funds. #POS
## 337 @NotTrump1946 9/10 believe the answer 3 based on principals from common core math... 🤣
## 338 @RAGINxCAJUN @PATRIQT_1776 @LadyOfTheOcean1 @ConcernedinPV @medwoman1 @Brian_Alford @_Kel_Varnsen_ @1Quetzalcoatl1 @Merry_Hippie @Lastplace_champ @TheOldPoet @Vickie627 @ahrehead @newsjunky2 @laylow88861429 @WokeSolarPanels @StrokeyStratfo1 @PoliticsPot @chrisg409ubc @giddy_bunny @BSHerrle @RickySi16087724 @ziggystardad @JonSmith922 @PaulStetson13 @TaiDecker @drlamb97 @SteFan40857266 @Emma34770971 @oilyslick1 @CamyS_2016 @Sinner_Lilith @TSbark @nikstift @moose57579 @JDW714 @Patrici76267702 @GQPklepto @Rocket_1981 @Renzics @Forseti_Pazzo @ziggystardad7 @417craig @atumpkins11 @thedemorats @mathmomma24 @MamaLouies @MistressRedWasp @trabriverman @marceelias You know common core hasnt been around as long as we’ve been out of school, right?\nDerp
## 339 Come on @Twitter...\nhow can I be up 160.2% on impressions\nbut down 22.3% on Profile visits...\nStop using the common core math...\nokie dokie...
## 340 Same people whose math skills were derived from common core... https://t.co/BL8a0KeBaA
## 341 @NVBats https://t.co/al93dWoaZC One potent reason why the testing juggernaut must stop. #CancelTheTests \n@SecCardona\n@usedgov\n@NewMexicoBATs\n@ColoradoBATsA
## 342 @RatLife8 @AgtThereal @Jeff23Williams @XSteveSchmidtX @SDGirl91 @BuckeyeBitch @BitchBuckeye @KimbetheStylist @SexiFkNpatriot @Glitter_Pigeon @MuffConnoisseur @PEno001 @judytgolf @TheSteveKon @DayBump @PAYthe_PIPER @PhillyFan1221 @buschisbae @kimmagagal2 I see you use common core math...
## 343 @NalaWasHere Is this common core?
## 344 Common Core R.1 Guided Practice Slides Template *Can be used with any text!* https://t.co/hkJ6lBnjUs
## 345 We pay for 1000Mbps (1GB)... Now let's do some SIMPLE (NOT COMMON CORE) math..
## 346 @gyaigyimii JHS is still 3 years, SHS students will do only core subjects in first year, then they will choose either a career pathway or academic pathway to cover the second and third year, the new curriculum is called Common Core Program, it is yet to be rolled out
## 347 @MariaKChica @LLinWood @ABAesq @FBIWFO You must be a product of common core!\n\n80mil + 74 mil = 154mil\n\nThere are 133mil registered voters.\nThat means there are 21mil more votes than voters...
## 348 @brownandbella Common Core is the why...I knew they didn’t get it when a guy told me there are no formulas or letters to solve something that was definitely algebra 😂
## 349 Where should we devote “the time, resources, innovative energies, and focus”, asks the #CommonCore. Perhaps it is not in the places we have previously thought important.
## 350 @LionTedPride Yeah, pretty much. Must be Common Core math.
## 351 common core taught me that 2+2 is 5 and you expect me to listen to math rock?!
## 352 @rgiasullo @RickyTheGee @KellyannePolls I can answer that. This is what happens when "common core" math is used. Zimba must have been smoking crack the day he came up with it.
## 353 @Jbuzzdc @boston_camera @LeeHolly81 @realDonaldTrump While you swallow CNN, ABC, CBS, NBC, BCC hook, line and sinker. No, buddy. I don’t watch FOX either. I think for myself, read my law books, educated prior to the advent of Common Core Indoctrination. And I NEVER called You people classy. \U{01f92e}
## 354 @LeBatardShow Justin Rose, looks like the guy, who teaches his kids common core math
## 355 @NYGovCuomo "Safe and Effective". ~1000 DEAD. ~16,000 injured. TWO DOZEN known side effects!\n\n You must be using Common Core math. https://t.co/bR76yb0ELl
## 356 @SqwackOps @hackerb0t I'm from before common core... So maybe the libtards changed the order of operations.
## 357 @ZubyMusic 🤦♀️🤷♀️🤔Did they use Common Core math to make this calculation? /s
## 358 @newsmax I call bullshit. Did you use Dominion machines to count that or covid counters. Or common core math?
## 359 @ThatTrumpJew @aranom12 That’s some common core there!
## 360 @JoeSilverman7 As much as Common Core education has been the bane of history for this nation, I think we have found a an Achilles Heel.
## 361 @bebe1969 😂🤣😂🤣 he must be using common core math!!! https://t.co/4TeuxI2xp4
## 362 @LCademartiriLab Absolutely agree. The common core math in the US is actual functional, useful math. It takes humility to recognize that just because one has difficulty helping your 2nd grader complete math HW, it doesn't mean the curriculum is messed up...
## 363 @JohnCena @MountainDew Common core count or Fauci count?
## 364 @ElZach99 @wutrain @EdWCVB @WuWCVB @EmilyRooneyWGBH @BosPublicRadio Mayor can tackle 3 factors: facility, method, educator. Fancier classroom (facility) & Common core (method) didn't work. Teacher Unions & low wages prevent improvement to educators. Best low cost method is large tutoring centers filled w/ Grad students to overcome bad teachers.
## 365 @latimes @GavinNewsom Gav must be using common core math. Your allocated vaccination distribution percentages do not add up or make sense.
## 366 58%?! wtf what kind of dumbass liberal, common core math is this? https://t.co/DPdYQ7NUJ7
## 367 @BernieSanders tries to solve Gov caused problems, by giving Gov more power over the problems it caused.\nDoing the same thing, expecting different results is the definition of?\n#DemocraticSocialism #Insanity #FreeTuition #TuitionFree #FreeCollege #CommonCore #CommonCoreCollege https://t.co/G75wLQTBes
## 368 @heatboss1 They're definitely proof that common core doesn't work.
## 369 @Nurse2b71 Made by adults who never took Common Core & failed because previous ed produced generations of memorizers instead of thinking problem solvers!
## 370 @Julius_Kim I use commas.\n\nI multiply the old way no common core crap. \n\nI add multiple numbers by 10s then carry over.\n\nI spell theatre for going to plays, theater for movies.\n\nSo no, I won't come at you; however, sometimes, twitter makes me have to adjust to the times, but I try to adapt.
## 371 The teacher is credentialed, but has some degree of independence (hates common core, quit public school.) I received an email today that the kids are starting a current events opinion writing unit next week. Now, my spouse and I are very apolitical libertarians. We value ideas
## 372 Download Mobi Alebra 1 Common Core Practice and Problem Solving Workbook: Complete Daily Support >> https://t.co/1fDIt6y5rh
## 373 @LisaA311 Just like $1,400 = $2,000 and 3 feet = 6 feet. It's the New Math they teach in Common Core.
## 374 @ZubyMusic I really miss the good old days when people had common sense and there was no Common Core.
## 375 @WorcTeaParty Should say "Bribed to use ANY textbooks!" WHY pick on Common Core???
## 376 Tenses: Past Tense Verbs Interactive PPT Grades 2 - 5 Common Core https://t.co/9pBTk6JHXh
## 377 @SJonNantucket @haroldmchasen @awstar11 How in the hell do they come up with 43% weighted sample for Republican with the actual number is 33%? I know common core math is absurd, but I didn’t think it was this bad. The actual numbers show they had to sample 63% democrat just to get 59% approval. Sad
## 378 @lindseymburke @InezFeltscher No education success at all; quality of U.S. ed was decimated by Common Core, & now Obama Duncan's cradle to career push to indoctrinate, not educate students is in full flower. Defund the entire U.S. Department of Education and give all rights to the states where they belong.
## 379 @sandyleevincent Common core is a bitch, too. Regular math had me screaming and crying in frustration; that shit would have seen text books flying through windows 🤦🏾
## 380 Listen: I got all As in math growing up, but it wasn't until going back to school for my Masters in Education and learning Common Core that I was able to do things like multiply large sums in my head.\n\nIf someone rails again Common Core, they're just showing their foolishness.
## 381 Actually, don't end rant. CANCEL COMMON CORE AND STATE ASSESSMENTS, FIGHT TO PUT PLAY BASED INSTRUCTION BACK INTO YOUR KINDERGARTENS, AND START WITH UNIVERSAL CHILDCARE AND PRESCHOOL FOR ALL (and that's just the beginning)
## 382 While 98% of America thinks this gurl is fucking stupid. I guess she's not well versed in simple math. That's why Republicans invented Common Core. Because doing things the easy way, was just to hard... https://t.co/MngA3EjEXY
## 383 @hrkbenowen Must be that common core math
## 384 According to Dems Riots Killings Illegal migrants emptying jails is all peaceful demonstrations.!! But All Trump Supporters Are terrorists.!! Go Figure?? Common Core Politics??😂🤣😂🤣\nhttps://t.co/bEci2b5RKO
## 385 @Markg88318828 @DwyerMcnally36 @mustxco2470 @1PatriotForLife 123 Million people were registered to vote, Trump got 74 Million accountable votes, Biden got 80 Million unaccountable votes, that totals 154 Million votes...that over 21 Million votes over what was registered...this ain’t common core math son, the election was a hack
## 386 @Bexnoell @POTUS Common core has taken away a Teachers ability to customize their teaching style and robbed them of innovation. Which has combined to drive US schools to the bottom of the industrial world rankings.
## 387 @TeachersOnFire Third, it was the work of the NCTM that also emphasized problem-solving and Standards. . . but not the awful common core standards that undermine true mathematical teaching and learning. It taught me, too, to consider these 4 elements: Discourse, Analysis, Tasks, and Environment.
## 388 @BalloKat @GlennKesslerWP Bless his heart. Common core math strikes again.
## 389 @ANGIES_DREAM Gotta ffinish that, but I didn't know Bill Gates leveraged in common core math here, I bet most parents wtf at it https://t.co/v4TcdPzjWq
## 390 Ignite the curiosity in your students! #teaching #educator #education #edchat #classroom #edtech #globaled #elemchat #mathchat #artsed #earlyed #middleschool #highschool #curriculum #commoncore #TeacherTalkTuesday #TeacherTwitter #K12 https://t.co/di1rEduRpC
## 391 @GholdyM: to WHOM is the common core common to and to whose core? \n\n👀\n\n#EquityisLoveinAction \n#EC21
## 392 Calling all #Autobody Techs: OC's first common core curriculum intake for level 1 starts April 19 at #okanagancollege. Only a few seats left! Apply for free online here: https://t.co/9zkfXBYxJY #TrainingBC https://t.co/MPga84E2mx
## 393 @ToberLana @EnemyOfTheLeft @walkawayAUS @lupash8 @BriteEyes8 @P8riot_1776 @PatPenn2 @marylene58 @DonnaWR8 @Easterndmondbk @BluehandRising @MsSpy007 @RealJamesWoods @realDonaldTrump @anthonymentill4 @OANN @JudgeJeanine @GovMikeHuckabee Ma'am, you ever heard Dr. Duke Pesta?\n\nI'll post the video as soon as it's ready. Could be 30 minutes. Common Core is Bill Gates and it's not meant to educate anyone.
## 394 Obama's common core math https://t.co/LL6IbmpyJg
## 395 Sounds like Common Core and Race to the Top, doesn't it? \n\nProbably just a coincidence.\n\n https://t.co/HDMRM6ocoB via @collegefix \n\n#stopHR1814 #StopHR400 #TCOT #StopCRT #PatriotsUnite
## 396 Transformations of functions is a key concept in common core. In school, they teach each function separately, but with finals coming up, I wanted to combine all of them as a cumulative review and to show that all these transformations work exactly the same https://t.co/3oK7kcrbHS https://t.co/3hMuiIqIq2
## 397 @XRP_OWL I despise common core, my kids have had so many melt downs over homework. I agree with the bullshit sentiment.
## 398 @Forgotso @thehill Common core has failed us
## 399 Have #Common-Core Standards ruined our children's #handwriting? https://t.co/KGjSwVIJW9
## 400 WAIT I THINK YOU MISSED A DECIMAL POINT COMMON CORE MATH MUST BE YOUR SPECIALTY 🤔 https://t.co/TWav5vcFx9 https://t.co/WPJTuZ47hJ
## 401 @megynkelly Remember they were behind common core also, they want to dumb down the American Children, will make it easier to control the masses when they have no knowledge. Communism. @megynkelly @BillGates
## 402 Divide and Conquer: An Adult Course in Common Core Division. Step-by-step guide for understanding and applying Common Core division concepts. This course contains videos teaching new ways to divide that students are learning in school today.\nhttps://t.co/W3YWh462d7\n@MAXMUSICK https://t.co/rfceLny2OS
## 403 Instead of all the common core shit, can we just make sure that the next generation understands that 1:1000 >> 1:500,000 > 1:1,100,000? #MakeMathGreatAgain https://t.co/ZdMUSHCLb1
## 404 @openpdxschools @NYC_stateofmind @angrybklynmom As a former middle school math teacher this sadly comes as no surprise. Dumbing down of Amer schools started with Bush & cont. with Common Core. No rote memorization allowed, not understanding the brain eventually catches up with the info. Parents should be terrified & pissed.
## 405 @EnigmaMachine9 @lineReedline @realestshady @DennisWRonan @covariantly @DoEcorruption @AngieSath @ConceptualJames @AndrewYang You one of the geniuses that design common core? If so it explains why you sound so dumb.
## 406 @TruthWins22 Like Trump, they share one common, core value. \n\nTheir entire, combined life-accomplishments amount to one thing: Being born white in America.
## 407 @realchrisrufo Okay, officially offended; common-core @usedgov has gone too far; pathetic compromised amongst USA-Citizens
## 408 @assetidentity Yep; it really does. My daughter hated common core so I taught her the “old” way and it clicked immediately and now she excels in it (probably better than me)
## 409 “Common sense isn’t common.” Something my friend said she learned from college that really stuck with me. There’s common core for schooling but two people can sit in the same physics class and one person will remember the KE formula and one person won’t.
## 410 When Betsy DeVoss reaches her inflection point, things must be really bad. Also if the US had implemented Common Core 35 years ago, we’d have a lot more critical thinkers out there. https://t.co/SdrXzlt5Rb
## 411 @Stfunell_ Common core working
## 412 @RepKinzinger Your party now seem to be The retrumplikan party 😩 it’s just sad and terrible that so many of the republicans bend over to this disgrace of a man. His morals are so corrupt it’s mind blowing. For sure Trump has NO common core values with republicans.. He has no values or moral!
## 413 @EsterAgain @DeAngelisCorey Common Core is standards, only!\nPearson won a contract for TESTING software! Local districts have always negotiated contracts for textbooks from the same publishers.\nData collection is GOV mandated independent of CC!\nhttps://t.co/5M7YHyJtMX\nWhy are legitimate sources ignored?
## 414 Have #Common-Core Standards ruined our children's #handwriting? https://t.co/KGjSwVIJW9
## 415 @BillGates Common Core hasn’t improved education at all, correct?
## 416 Humans and animals will continue to emit carbon. There is no way to be net zero unless you use common core math. And if course #2 in my previous Tweet is not viable. So there are two viable options.
## 417 @JustSayingWhat1 @lesgantssexuels @DarwanD1 @GayerestHaunter @starstrucksugar @TheGod_Particle @RJDownard @MikeSut31468809 @odktiger @AenesidemusOZ @Onychom @AllistairGraham @Christgodtweet @coelleengerhard @alllibertynews @cornbrolio @Glad2bAtheist @BootsRnecessarY @DarthKiller2 @revilokesh @IngridIngwah @intelligent50 @show_tao @A_Damned_smith @djangounchaine8 @Freewillburnin @deepinsights19 @PObdura @EBatterson @ty2010b @rpratt039 @rkdoctr @Reid_CO @StandBackUp2 @rupejonner2 @Fr1nk3 @RomagnoliEsque @randolf828 @PaulG16808960 @YICYAC @mortisford @A__Stout @DanielM62699792 @zythophiliac @zetetic2017 @BenefactrChurch @LeahHen18676712 @Jim1810 @The_Real_BiM @SteveTiger999 quality used to be in the schools common core and teachers unions are just a few ..my 3 childre are in their early 30s and i fought constantly with their teachers over the lack of teaching n terrible curriculum
## 418 #3rdGrade Common Core Aligned #Math #TestPrep Packet with Answer Key by MrTechnology on #TeachersPayTeachers #edchat #education \n https://t.co/Ce3EIkekJv
## 419 @BillGates Ruined education with your Common Core experiment, now ruining the middle class with the plandemic. Population control via vaccines. Evil. https://t.co/cnW9yoXRK1
## 420 Probobly an unpopular opinion but I think the Common Core curriculum is a good idea but it's been implemented by morons.
## 421 @Braves @Braves. It's "its" not "its'". Its is already possessive without the apostrophe. Common core English, perhaps?
## 422 Common core https://t.co/3DvUmzfwZ5
## 423 i'll never forgive my elementary school for teaching me common core methods to do math problems
## 424 @brianjoralvarez Common core.
## 425 @JohnBKing @GPayneEDU @usedgov @arneduncan @MargaretEdu @rodpaige @LamarAlexander @EADRoadmap You promised us Common Core would raise achievement. You lied. Your time as Ed Secretary was a colossal failure, now you’re just a grifter. Wherever you go, failure follows. Stay away from children. https://t.co/vzGt7Pu0Tp
## 426 @MDucci791 @BuzzPatterson @GOPLeader You look a little old for common core math so I can only attribute your counting skills to shit for brains ... you should probably have that checked out.
## 427 @CBSNews They need to evaluate k-12 drop this common core shit that doesn't work, teach skills that kids can use after high school. Clinton pushed for free community college which can be good tech programs immediate employment with good $$
## 428 Replace Common Core/Teachers Union teachers with those who will restore truth in education to teach our children HOW to think free again (not WHAT to think). Restore true American history-it has been rewritten to promote New World Order since 1984. Be'ware Common Core teachers! https://t.co/EwrvQ0zIQc
## 429 @politickingapp Did they leave a zero off? This would make more sense if it was 14,000 #commoncore #math
## 430 @RastaRedpill That's some common core thinking right there lol
## 431 I tutored kids before and during the Common Core math roll out... the mainstream "gifted" kids were always going to grasp the concepts, but the ones who were "struggling" got so much more in their math toolbox with Common Core.
## 432 Join us today at 10am PST for this #preschool livestream math class, where we’ll cover common core standard skills using visual models and simple strategies based on the understanding of what Equal means!\n\n Streaming on: https://t.co/vyvQIUXNAv #math #preschoolactivities #daycare https://t.co/jmWqGjgy5r
## 433 @graceearthur @JackPosobiec @washingtonpost Common core math
## 434 The Common core educational system must fall also!!! https://t.co/BziNKe1bWH
## 435 If You Liked Common Core, You’re Going To Love Joe Biden’s Civics https://t.co/xswgN7pVYs
## 436 Democrat Common Core Math https://t.co/uurCteNBdP
## 437 Review Online Prentice Hall Literature: Common Core Edition => https://t.co/pHoRrqTJvp
## 438 i play jumpstart, but math's hard\nand i can't stand learning anything\n i like when grown-ups call me smart\nbut i'm dumb, so i won't add sums up anymore\nand i barely even care that i'm forgetting all the common core
## 439 1/ In this week's @TimesHigherEd Asia newsletter: \n#Singapore moves to real interdisciplinarity: @NUSingapore has a new College of Humanities \n& Sciences. @NTUsg will use a new common core curriculum this fall.\nRead: https://t.co/E2GcXfKB5s\nSign up: https://t.co/XV2XMpbpPy https://t.co/AWyg7KS7Tl
## 440 @TimSabean Common core??
## 441 Bush or anybody in favor of Common Core is a total net worth after all.
## 442 @OceanEyes50 So, liberals gave us common core math, 175+ genders, life never actually begins until we decide it does (so, is murdering the parent considered abortion?), and made up words. Got it. Note to self- avoid people who qualify as #Dumbasses.
## 443 Have #Common-Core Standards ruined our children's #handwriting? https://t.co/KGjSwVIJW9
## 444 @JennaEllisEsq How about we just bring back Art, Music, and Shop classes. Also go back to teaching cursive writing and math where there is only one correct answer and not common core.
## 445 ICYMI Couldn't agree more @jamesbshort #CurriculumPL will make the difference. Read: "In the years ahead, we should double down on investments to help teachers leverage the high-quality CCSS-aligned curricula .. ” https://t.co/4gj16AGBsf For guidance see https://t.co/iezEeUsmeh
## 446 @ilovejohnkimble Looks like that new common core math
## 447 “The Common Core had a large positive initial effect on economically advantaged students but no detectable initial effect on economically disadvantaged students.” #OpportunityGap #EdResearch https://t.co/XYK6jeZyvT
## 448 @mastrovv @zexpray Funny! Just to be clear, Common Core says teach order of operations but it doesn't define or explain it.
## 449 @SeanFulce2040 Common core math at work
## 450 Perhaps what we’re seeing is an opportunity to renew a common core—democracy as a peaceable union and not a race to become our most virulent selves. Humility would be a great trait to see in our leaders about now.
## 451 @megynkelly @RealSaavedra Gates and/or his foundation developed Common Core education for primary schools. It failed to bring up standards in all areas, including Common Core math which was so puzzling that even parents couldn't understand it. No wonder he doesn't want student's math work checked!
## 452 @MajorPatriot Lol. A Common Core problem.
## 453 @blonde_opinion I think it's taught on common core math!
## 454 Achieve great things with the help of TAS Learning Center \n\nTAS will help your child tackle anything! \n\nWe will help with:\nSAT&ACT Prep\nSTEM Based Curriculum\nReading Comprehension\nCommon Core Prep\n\n#TAS\n\nJoin us today!\nCall 1(888) 827-8188 \nVisit https://t.co/m23XBH2ScK https://t.co/IOMx8eWrmG
## 455 @buchananshelle3 @DianeRavitch There will be a leadership change which will nix all that was done in the name of $ (Common Core). The kids will be freed from the shackles of testing as more begin to see that it is part of an old world which hurt more than helped.
## 456 @BensHoops Common Core reference on NBA Twitter = one like
## 457 @ActNormalOrElse I took proper numbers in college for my common core math credit actually
## 458 @stacEight common core vs. home skool?
## 459 Common core math. \n\nThank Barry for this! https://t.co/tNbrnePdCI
## 460 @1ZetaBlu_ \U{01f974}\U{01f974}\U{01f974}\n\nI wonder what year schools switched to common core.
## 461 @nytimes @sanmiguelillo I wish I could believe this, I really do. But the cognitive dissonance is just too heavy a burden. The numbers just don't add up unless you use common core math.
## 462 @aelfred_D Common core?
## 463 BTW, wouldn’t this make a great common core math question? I thought it might help. 🤣
## 464 @w_terrence Obama's common core math
## 465 @MalloryGates14 @MikeMooreDO North Carolina ditched Common Core Math standards in 2017. \nSince then, the state has gone from 2 points below nation average on EOG math to 3 points above.
## 466 @Kurimuzonporisu @Seekerotruth Rejecting Common Core is silly when not understanding it, as most politicians who oppose it!\nWiser to discus what grads need & how to get it than oppose TEN YEAR OLD standards like CC!
## 467 @JesseRodriguez @HotlineJosh Hahahaha! Common Core math polling.
## 468 This is how the people who hate public schools attack. They lump anything that makes them unhappy under a label (critical race theory) and demonize it on repeat. Then scare patrons and punish districts. Remember the attack on “common core?” Same playbook. Fearmongering. Shameful. https://t.co/GfF3r7tBih
## 469 EBOOK Download Free 5th Grade Math Workbook: Common Core Math Workbook >> https://t.co/WPzA8gbCuB
## 470 @CNN @ForecasterEnten Common core?
## 471 @ShreyaJainNYC But…but…but…that’s not part of common core?!?!?!?!?! How would we ever survive with such a USEFUL skill?!?!?!?! Ooooooh the horror!! — please shout this from the rooftops, this is so spot on
## 472 Get Free 7th Grade Common Core Math: Daily Practice Workbook - Part I: Multiple Choice | 1000+ Practice Questions and Video Explanations | Argo Brothers -> https://t.co/YbNtCbJYMp
## 473 Because they was feening to teach Common Core like tf https://t.co/y5H6CPHOem
## 474 @EsterAgain @DeAngelisCorey Amazing ppl fight AGAINST 10 year old Common Core instead of discussing & supporting efforts to improve\nShould check out textbook adoption. Here is California.\nhttps://t.co/5GFR0ktZ9t\nhttps://t.co/4d9vtuMXm7\nhttps://t.co/yd7024y9TS
## 475 The research suggests that as organizations look for meaningful ways to invest in their workforces, the most effective approaches have a common core: opportunity.\n\nhttps://t.co/xkTCL2Tu9j
## 476 @CrnchyMama @MCPS Nope. My friend I student taught with in hoco was saying they are hiring interns like crazy. It’s BAD. I don’t think I remember it being like this back in the common core days. I’ve never seen anything like this
## 477 @rdsathene @allblues18 @DJeffreySmith @YELLAHKWEEN @zei_squirrel Point taken - YA writers say unkind things about Bill Gates's "Common Core" teaching theories, which discourage fiction (especially Fantasy & SF) & push nonfiction as the only "valid" books for schools. (I'm sure somebody's about to GatesSplain how I don't understand CC at all!)
## 478 @EdLatimore The problem with this example is the grouping is poor. If broken up like algebra, and the pieces swapped, it’d be more clear! The common core thing that threw me was the division. It’s interesting to see an alternative, but not sure it’s better; maybe leads to more understanding?
## 479 @donwinslow They used common core math
## 480 @catturd2 Common core
## 481 @RyanAFournier Common Core strikes again
## 482 Download Kindle Common Core Achieve, GED Exercise Book Reading and Writing (Ccss for Adult Ed) >> https://t.co/Cuymf62yof
## 483 Get rid of common core. It's total garbage.
## 484 @CCMathResources @JonahDispatch If only Amanda what’s-her-face hadn’t been taught Common Core English lol\n\n(Nah, plenty of people are mastering English and math despite Common Core - this is on Harvard for admitting her, not you guys.)
## 485 @AnneWheaton I know people talk about Common Core math, but that isn’t how it works!
## 486 When end of days homeless people preach \n\n#moleg always dismiss their warnings! \n\nWhen a man that used CAPITALISM to become Filthy Rich \n\ngave our children common core math \nTaught them to hate their country aka antifa idiots \n\nTells a newspaper his truth \n\nDO YOU DISMISS IT ?? https://t.co/WLNhTrv341
## 487 >> 7th Grade Common Core Math: Daily Practice Workbook - Part I: Multiple Choice | 1000+ Practice Questions and Video Explanations | Argo Brothers <<\n✔ Click Link Here >>> https://t.co/ggx1mTngMO 7th Grade Common Core Math: Daily Pract
## 488 @EdRushman @KevinKileyCA All parents want their kids out of school that teach lies to kids regarding history, teach the dumb-down common core, teach gender confusion, and critical race lies! This is not a conservative issue, it is the right thing to do
## 489 @randybias What's the matter, you can't do common core? 3+2y=purple because 4 stacks of books is 3 shoe boxes. What's so hard about that?
## 490 the comment section of this tik tok pisses me off bc this is literally what we teach with common core but people still bash it 🙃\n\nhaving kids learn in a conceptual/concrete way is always gonna have a better outcome than abstract thinking! it should always come first https://t.co/uYxg5AM9LN
## 491 @BrianAlderton32 @WolfVanHalen Feels like common core all over again. 😬
## 492 L3 PAEDIATRIC FIRST AID AWARD £69.50, 12 HR BLENDED LEARNING MADE UP OF 1 CLASSROOM DAY COMBINED WITH AN E-LEARNING MODULE and btw don't forget our brilliant bundle with Common Core Skills! Choose from 15th or 22nd May + dates in June onwards.\nhttps://t.co/j3krCaAmMA https://t.co/JYXsq1HRMm
## 493 Obama's common core math at work... https://t.co/k7TnMv6fdy
## 494 #Election2020 \n\n#TheWorldIsWatching \n.\n\nAphrodisiacal logic. \nBoom.\n\nReminder: The rest of the world was not bedazzling common core. \n\n#Trump2020 🇺🇸 \n#AdvancedPlacement \n\nHow do you think we got here? https://t.co/mO3vjDteq1
## 495 Nope. Just a person who understands common core.\n\nAnd math. https://t.co/XPnX8CuVj1
## 496 @D0MXNXQUE I just tried a few math problems. Common core makes solving the problem more complicated than necessary. The traditional way >>>> Common core
## 497 @BurgessOwens @billycrash The people who see ‘racism’ behind every shadow, are the real racist.\n\nWhat the sharia/communist are trying to do is not hard too see. \nIt’s been their strategy since the very beginning.\n\nCommon core, ‘commie education’ put blinders on many people= group think=control.
## 498 @TimRunsHisMouth Maybe doing nothing is better than imposing BS common core curriculum or imposing even more Leftist indoctrination programs. But- she should have worked to advocate for more education and less SJW crap. Our younger generation is educated but ignorant.
## 499 All the tests: $ from districts\nAll the prep materials: $ from districts \nAll the “common core” aligned curriculum: $ from districts\nAll the “cool” programs and tech now required: $ from districts.\n\nAcross the country. \n\nProfits off kids. That’s who #BillGates is.
## 500 @TeachTheClassic @ConceptualJames Every new trend comes with a wave of consultants/trainings - Individualized Instruction, Multiple-Intelligences, No Child Left Behind, Whole Language, Common Core, Flipped Classroom and on and on and on. \nTons of $ to be made. \nIt's an amoral system. 2/2
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## 76 {0, 0, 1.4, 0, 0, 0, 0, 0, -1.4, -1.5, 0, 0, 0, 0, 0, -2.6, 0, 0, 0, 0, 0, 0, 0, 0, 3.2, -2.1, 0, 0}
## 77 {0, 1.175, 0, 0, 0, -1.35, 0, 0, 0, 0, 0, 0, 0, -1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 78 {0, 0, 0, 0, 0, 0, 0.7, 0, 0, 0, 0, 0, 0, 0, 0, -2.1, 0, 2.25, 0, 0, 0, 0, 0, 0}
## 79 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.11, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.7, 0, 0}
## 80 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -0.7, 0, -1.2, 0, 0, 0, 0, -1.8, 0, 0, 0, 0, 0, 0, 0}
## 81 {0, 0, 0, 1.5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 82 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -2.3}
## 83 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 84 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -2.5, 0, 0, 0, -1.5, 0, 0, 0}
## 85 {0, 0, 0, 0, 0, 0, 0, 0, -1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3.2, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.7}
## 86 {0, 0, 0, 0, 0, 0, 0, 0, 1.8, 0, 0, 0, 0, 1.9, 0, 0, 0, 0, 0, 0, 0, 0, 0, -2.3, 0, 0.4, 0, 0, 0, 0, 0.3, 0, 0, 0, 0, 0, 0, 0, 0, 2.4, 0, 1.1, 0}
## 87 {0, 0, 0, 0}
## 88 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 89 {0, 0, 0, 0, 1.7, 1.4, 0, 0, 0, 0, 0, 0, 0, 1.7, 0, 0, 0, 0, 0, 0}
## 90 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 91 {0, 0, 0, 0, 0, 3.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.7, 0, 0, 0, 0, 0, 0}
## 92 {0, 0, 0}
## 93 {0, 0, 0, 0, 0, 0, 0}
## 94 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 95 {0, 2.3, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 96 {0, 0, 0, 0, 0, 0, 0, 0}
## 97 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 98 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -2.1, -1.3, 0, 0, 0, 0, 0, 0, 0, 0}
## 99 {0, 0, 0, 0, 0, 0, 1.7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.5, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 100 {0, 1.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -0.5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 101 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3.37135, 0, 0, 0}
## 102 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 103 {1.7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 104 {0, -0.2, 0, 0, 0, 0, 0, 0, 0, 0.75, 0, 0, 0, 0, 0, 0, 0, 0, 0.3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -2.94555, 0, 0, 0, 0, 0}
## 105 {0, 0, -2.393, 0, 0, 0, 0, 0, 0}
## 106 {0, 0, 0, 0, 0, 0, 0, 0.8, 0, 0, -1.5, 0, 0, 0, 0, 0, 0, 0}
## 107 {0, 0, -1.2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -3.4, 0, 0, -1.406, 0, 0, 0, 0, 3, 0, -2.5}
## 108 {0, 0, 0, 0, 0, -2.9, 0, 0, 0, -2.7, 0, 0, 0, 2, 0, 0}
## 109 {0, 0.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.1, 0, -2.3, 0, 0, 0, 0, 0.8, 0, 0, 0, 0, 0, 0, 0, 0}
## 110 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 111 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -0.4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 112 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 113 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.3, 0, 0, -1.7, 1.4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.6, 0, 0, 0, 0, 0}
## 114 {0, 0, 0, 0, 0, 0, 0, -1.5, 0, 0, 0, -0.9, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.1, 0, 0, 0, -1.4, 0, 0}
## 115 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2.2, 0}
## 116 {0, 0, 0, 0, 0, 0, 0, 0.15, 0, 0, 0, 0, 1.4, 0, 0, 0, 0, 0, 0, 0, 0}
## 117 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.5}
## 118 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.518, -3.3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 119 {0, 0, 0, 0, 0, 0, 0, 3.083, 0}
## 120 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 121 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 122 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 123 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 124 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.3, 0, 0, 0, 0, 0, 0}
## 125 {0, 0.75, 0, 0, 0, 0, 0.85, 0, 0, -3.45, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2.882475, 0, 0, 0, 0, 0, 0, 0}
## 126 {0, 0, 0, 0, 0, 0, 0, -3.1, 0, 0, 0, 0, 0}
## 127 {0, 0, 0, 0, 0, 0, 0}
## 128 {0, 0, 1.2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.2, 0, 0, 0, 1.5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0}
## 129 {0, 0, 0, 0, 0, 0, 0, 0.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2.133, 0, 0, 0, 0, 0}
## 130 {0, 1.7, 1.5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -2.6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.4, 0, 1.8, 0, 0, 0, 0, 0, -1.2, 0, 0, 0}
## 131 {0, 0, 0, 0, 0, 0, -2.7}
## 132 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.7, 0, 0, 0, 0, 0, 0}
## 133 {0, 0, 0, 0, 0, 0, -2.5, 0, 0, 0, 0, 0, 0, 0}
## 134 {0, 1.2, 0, 0, 0, 0, 0, 0, 3}
## 135 {0.6, 0, 0, 0, 0, -0.85, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.95, 0}
## 136 {0, 0, 0, 0}
## 137 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 138 {0, 0, 0, 0, 0, -1.5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2.4, 0, 0, 0, 0, 0, 0, 3.2, 0, 0, 0, 0}
## 139 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2.35}
## 140 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 141 {0, 0, 0, 0, 0, 0.3, 0, -2.633}
## 142 {0, 0, 0, 0, 0, 0, 0, 0, -2.5, -1.6, 0, 0, 0, 0, 0}
## 143 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 144 {0, 0, 0, 0, 0, 0, 0}
## 145 {0, 0, 0, 0, 0, 0, 0.4165, 0, 0, 0, 0, 0, 0, 0, -1.7165, 0, 0, 0, 0, 0, 0, 0, -1.6995, 0, 0, 0, 0, 0, 0, 0, 4.6245, 3.9495, 0, -4.9995, 0, 0, -3.9495, 0, 0, 0, 0, 0, 0}
## 146 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 147 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 148 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 149 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -0.6, 0, 0, 0, 0, 0.15, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 150 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 151 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3.0637, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2.7, 0, 0, 0, 0, -1.3}
## 152 {0, 0, 0, 0, 0, 0, 0, 1.5, 0, 0}
## 153 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 154 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.5, 0, 0, 0}
## 155 {0, 0, 0, 0}
## 156 {0, 0, 0, 0, 0, 0, 1.5, 0, 0, 0, 0, 0, 0, 0, 0, 0, -2.5, 0, 0, 1.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 157 {1.2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 158 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 159 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -3.293, 0}
## 160 {0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 161 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.2, 0, 0, 0, 0, 0, 0, 0, 0}
## 162 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 163 {0, 0, -1.8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -2.1, 0, 0, 0, 0, 0, 0, 0, 0, 1.7, 0, 0, 0, 0, 0, 0, 0, -0.666, 0, 0, 0, 0, 1.207, 0, 0, 0}
## 164 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 165 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.9, 0, 0, -2.5, 1.2, 0, 0, 0.9, 0, 0, 0, 0, 0, -0.9, 0, 0, 0, 0, 0, 0}
## 166 {0, 0, 0, 1.036, 0.666, 0, 0, 0, 0, 0, 0, 0, 0}
## 167 {0, -0.5, 0, 0, 0, 0, 0, 0, 0, 0, -1, 0, 0, 0, -1.2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 168 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1.5}
## 169 {0, 0, 0, 0, 0, 2.3, 0, -1.4, 0, 0, 0, 0}
## 170 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -2.2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 171 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -3.7, 0, 0, 0, 0, 0, 0, 0, 0}
## 172 {0, 0, 0, -0.481, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 173 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 174 {0, 0, 0, 0}
## 175 {0, 0, 1.7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.8, 0, 0, 2.6, 0}
## 176 {0, 0, 0, 0, 1.5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 177 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -2.4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 178 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.9, 0, 0, 0, 0, 1.11, 0, 0, 0, 0, 0, 0}
## 179 {0, 0, 0, 3.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 180 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 181 {0, 1.1, 0, 0, 0, 0, 2.2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.4, 0, 0, 0, 0}
## 182 {0, 2.3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 183 {0, 0, 0, 0, 0, 0, 0, 1.9, 0, 0, 0}
## 184 {0, 0, 0, 0, 0, 0, 0, 0}
## 185 {0, 0, 0, 0, 0, -2.5}
## 186 {1.7, 0, -1.2, 0, 0, 1.9, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.7, 0, 0, 0, 0, 0, 1.2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 187 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 188 {0, 0, 0, 0, 0, 0}
## 189 {2.8, 0, 0, 0, 0, -2.5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.8, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 190 {0, 0, 0, 0.8965, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.95, 0, 0, 0, 0, 0, 0, 0, 0, 1.65, 0, 0, 0, 0, 0, 0, 0, 0}
## 191 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 192 {0, 0, 0, 1.7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 193 {0, 0, 0, 0, 0, 0, 0, 1.8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2.4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 194 {2.35, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 195 {0, 0, 0, 0, 0, 0, 0}
## 196 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.5, 0, 0, 0, 1.7, 0, 0}
## 197 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.326, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -2.7637, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 198 {0, 0, -1.8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 199 {0, 0, 0}
## 200 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 201 {0, 0, 0, 0, 0, 0, 0}
## 202 {0, 0, 0, 0, 0, 0, 0, 0, 0}
## 203 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -2.7, 0, 0, 0, 0}
## 204 {0, 0, 0}
## 205 {0, 0, 0, 0, 0, 0, -0.4, 0, -2.1}
## 206 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 207 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.7, 1.3, 0, 0, 0, 0, 0, 0}
## 208 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.4, 0, 0, 0, -0.5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 209 {0, 0, 0, 0, 0, -2.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, -0.4, 0, 0, 0}
## 210 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 211 {0, 1.7, 0, 1.9, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 212 {0, 0, 0, -0.666, 0, 0, 0, 0, 0, -1.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.3, 0, 0, 0, 0, 0, 0}
## 213 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.593, 0, 0, 0, 0, 0, -3.4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2.593, 0, 0, 0, -1.7, 0, 0, 0, 0, 0, 0, 0, 0, 1.9, 0, 0}
## 214 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 215 {0, 0, 1.6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.9, 0}
## 216 {0, 0, 0, 0, 0, 0, 0, 0, 0}
## 217 {0, 0, -1.57835, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.776, 0, 0, 0, 0, 0, 0, 0}
## 218 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2.5, 0, -2, 0, 0, 0, 1.3, 0, 0, -1.693, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 219 {0, 0, 0, -1.702, 0, 0, 0, 0, 0, 0, 0, 0, 0, -2.1, 0, 0, 0}
## 220 {0, 0, 0, -1.2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.2, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.888, 0}
## 221 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.5, 1.9, 0, 0, 0, 0}
## 222 {0, 0, -1.7, 0, 0, 0, 0, 0, -1.3, 0, 0, 0}
## 223 {0, 0, 0, 0}
## 224 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -3.9, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 225 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 226 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.1, 0, 0, 0, 0, 0, 1.7, 0, -1.3, 0, 0, 0, 1.5, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.6, 0, 0, 0, 0, 0, 0, 0, 0}
## 227 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -3.75}
## 228 {0, 0, 0, 0, 0, 0}
## 229 {0, 0, 0, 0, 1.5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 230 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.9, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 231 {0, 0, 0, 0, 0, 2.3, 0, 1.4, 0, 1.7, 0, 0, 0, 0, 2.2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 232 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 233 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 234 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.6605, -0.932475, 0, 0, 0}
## 235 {0, 0, -0.95, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3.75, 0, 0, 0, 0}
## 236 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 237 {0, 0, 0, 0, 0, 0}
## 238 {0, 0, 0, 0, 0}
## 239 {0, 0, 0, 0, 0, 0, 0, 0, -2.4, 0, 0, 0, 0, 0, 0, 0, -0.9, 0, 0, 0, -0.814, 0, 0, 0, 0, 0, -3.1}
## 240 {0, 0, 0, 0, 0, 0, 0, 0}
## 241 {0, 2.033, 2.033, 2.933, 0, 0, 0, 0, 0, -2.033, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2.033, 0, 0, 0, 0, 0, 0, 0, 0, -2.9564, 0}
## 242 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 243 {0, 0, 0, 0, -2.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -0.074, 0}
## 244 {0, 0, -3.5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.9, 0, 0, 0}
## 245 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.2, 0, -2.55, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -4.2, 0, 0, 0, 0, 1.65, 0, 0, 0}
## 246 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 247 {0, 0, 0, 0, 0, 0, 0, -1.7, 0, 0, 0, 0, 0, 0, 0, 0, 0, -0.3, -1.7, 0}
## 248 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -2.3, 0, 0}
## 249 {0, 0, 1.9, 0, 0}
## 250 {0, 0, 0, 0, 0}
## 251 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 252 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.8, 0, -2.5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -3.3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 253 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 254 {0, -2.5, 0, 0}
## 255 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.4, 0, 0, 2.5, 0, 0, 0, 0, 0.1, 0, 0, 0, 0, 0, 0, 0, 0, -1.7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 256 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -2.4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 257 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.9, 0, 0, 0, 0, 1.9, 0, 0, 0, 0, 2.2, 0, 0, 0, 0, 0, 2.2, 0, 1, 0, 0, 0, 0, 0, 1.9, 0, -2.833}
## 258 {0, 0, -2.5, 0, 0, 0, 0, 0, 0, -1.7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -2.3, -2.6, 1.5, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 259 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.1, 0, 0, 0, 0, 0, 0, -0.4, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 260 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, -1.4, 0, 1.5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 261 {1.3, 0, 0, 0, 0, 0, 0.9, 0, -0.5, 0, 0, 0, 1.5, -1.3, 0, 0, 0, 0, 0, 0, 0, 0, -2.2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 262 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -0.74, 0, 0}
## 263 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 264 {0, 0, -1.9, 0, 0, 0, 0, 0, 2.3, 0, 0, 0, 0, 0, 1.2, 0, 0, 1.8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 265 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.9, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0}
## 266 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 267 {0, 0, 0, 0, 0, 0}
## 268 {0, 0, 0, 1.7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 269 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.5, 0, 1.1, 0}
## 270 {0, 0, 0, 0.6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.518, 0, -0.5, 0, 0, 0.95, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 271 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -2.6, 0, 0, 0, 0, 1.9, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.9, 0, 0, 0}
## 272 {0, 0, 0, 0, 0, 0, 0, 0, -1.406, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -2.833, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 273 {0, 0, -1.2, 0, 0, 0, 0, 0}
## 274 {0, 0, 0, 0, 0}
## 275 {-1.3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 276 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -2.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 277 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.6, 0, 0, 0, 0}
## 278 {0, 0, 0, 0, 0}
## 279 {3.033, 0, 0, 1.7, 0, 0, 0, 0, 0, -1.3, 0, 1.5, 0, 0, 0, 1.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3.193, 0, 0, 0, 0, 0}
## 280 {0, 0, 0, 0, -1.393, 0, 0, 0, 0, -1.5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.793, 0, 0, 0, 0, 0, 0, 0, -1.2, 0, 0, 0, -1.6, 0}
## 281 {0, 0, 1.7, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.4, 0, 0}
## 282 {0, 0, 0, 0, 0, 0, 0, 0, -2.4}
## 283 {0, 0, 0, 0, 0, 0, 0, 0, 0}
## 284 {0, 2.3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.1, 0}
## 285 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.9, 0, 0, 1.6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 286 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 287 {0, 0, 0, 0, 0, 0}
## 288 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2.2, 0, 0, 1.5, 0, 0, 0, 0, 0, 0, 0}
## 289 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 290 {0, 1.3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 291 {0, 0, 0, 0, 0, -1.2, 0, 0, 0, 0, 0, 0}
## 292 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 293 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 294 {0, 0, 0, 0, 0, 0, -3.6, 0, 0, -1.3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 295 {0, 0, 0, 1.2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 296 {0, 0, 0, 2.1, 0, 0, 0, -3.4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.8, 0, 0, 0}
## 297 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 298 {0, 1.7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 299 {0, 0, 0, 0, 0, 0, 0, 0, -2.9, 0}
## 300 {0, 0, 0, 0, 0, 0, 0, 0}
## 301 {0, 1.8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.3, 0, 0, 0, 0}
## 302 {0, 0, -0.6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.15, 0, 0, 0, 0, 0, 0.9, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.85, 0, -1.1, -0.4, 0, 0, 0, 0, 0, 0, -2.55, 0, 0, 0}
## 303 {0, 1.3, 1.7, 0, 0, 0, 0, 0, 0, 0, 0, -0.666, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2.733, 0, 0, 0, 0, 0, 0, 0, 0, -2.7, 0, 0, -2.7, 0, 0, 0, 0, -2.7}
## 304 {0, 0, 0, -1.2, 0, 0, 0, 0, 0, 0, -2.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.5}
## 305 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 306 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.7, 0, 0, 0, 0, 0}
## 307 {0, 0, 0, 0, -3.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 308 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.998, 0, 0}
## 309 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.11, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 310 {0, 0, 0, 0, 0, 0, 0, 0, -0.222, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2.8, 0, 0, 0, 0, 1.6, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 311 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 312 {0, 0, 0, 0, 0, 0}
## 313 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.9, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 314 {0, 0, 0, 1.7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -2.4, 0}
## 315 {0, 0, 0, 0, 0, 0, 0, 0, 0}
## 316 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2.3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.1, 0}
## 317 {0, 0, 0, 0}
## 318 {0, 3.2, 0, 0, 0, 2.7, 0, 0, 0, 0, 1.6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 319 {0, 0, 0, 0, 1.45, 0, 0, 0, 0, 0, 0, 0, 0, -0.6, 0, 0, 0, 0, 0}
## 320 {0, -1.5, 0, 0, -1.998, 0, 0, 0, -1.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -2.3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 321 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 322 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -3.2, 0, 0, 0, -1.3, 0, 0, 0, 0, -1.7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 323 {0, 0, 0, 0, -3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -3.1, 0, 0, -1.3, 0, -2.7, 0, 0, 0}
## 324 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 325 {0, 0, 0, 0, 1.7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 326 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 327 {0, 0, 0, 0, 0, 0, 0, 0}
## 328 {0, 0, 0, 0, 0, 0, -3.693, 0, 0, 0}
## 329 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 330 {0, 1.5415, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.35, 0, 0, 0, 0, 1.5}
## 331 {0, -1.2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 332 {0, 1.7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.5, 0, 2, 0, 1.6, 0, 0, 0, 0, 0, 0, 0}
## 333 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2.5, 2, 0, 0, 0, 0, 0, 0}
## 334 {0, 0, 0, 0, 0}
## 335 {0, 0, 0, 0, 0, 0, 0, 0}
## 336 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -0.4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -0.4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 337 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 338 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 339 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.45, 0, 0, 0, 0, 0, 0, -1.8, 0, 0, 0, 0, 0, 0, 0}
## 340 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 341 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.2, 0, 0, 0, 0, 0}
## 342 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 343 {0, 0, 0, 0, 0}
## 344 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 345 {0, -0.4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 346 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 347 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 348 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.2, 0, 0, 0, 0, 0.8, 0, 0, 0, 1.7, 0, 0}
## 349 {0, 0, 0, 1.4, 0, 0, 0, 1.9, 0.9, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.8}
## 350 {0, 1.2, 2.2, 0, 0, 0, 0, 0, 0}
## 351 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 352 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 353 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.9, 0}
## 354 {0, 0, 0, 0, 1.5, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 355 {0, 1.9, 0, 2.1, 0, -4.033, 0, -1.7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 356 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 357 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 358 {0, 0, 0, -2.8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 359 {0, 0, 0, 0, 0, 0, 0}
## 360 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 361 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 362 {0, 0, 1.793, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.9, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.4, 1.2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.4, 0}
## 363 {0, 0, 0, 0, 0, 0, 0, 0}
## 364 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.1, 0, 0.1, 2, 0, 0, 3.2, -1.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -2.5, 0}
## 365 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 366 {0, -2.8, 0, 0, 0, -2.307, 0, 0, 0, 0, 0, 0, 0}
## 367 {0, 0, 0, 0.8, 0, 0, -1.7, 0, 1.4, 0, 0, 0, 0, 0, -1.7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -2.7, 0, 0, 0, 0, 0, 0}
## 368 {0, 0, 1.7, 0, 0, 0, 0, 0, 0}
## 369 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -2.3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.7, 0}
## 370 {0, 0, 0, 0, 0, 0, 0, 0, 0, -0.6, 0, 0, -0.8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.5, 0, 0, 0, 0, -0.7465, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 371 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -2.85, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.567525, 0, 2.1, 0}
## 372 {0, 0, 0, 0, 0, 0, 0, 0, -1.7, 1.4, 0, 0, 0, 1.7, 0, 0}
## 373 {0, 0, 1.5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 374 {0, 0, 0, -0.893, 0, 2.1637, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.2, 0, 0}
## 375 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 376 {-0.9, 0, -1.4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 377 {0, 0, 0, 0, 0, 0, -1.8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.15, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -3.75, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3.15, -3.15}
## 378 {0, 0, 0, 0, -1.998, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 379 {0, 0, 0, 0, 0, -2.8, 0, 0, 0, 0, 0, -1.6, 0, -2.1, 0, -2.1, 0, -2.6, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 380 {0, 0, 0, 0, 0, 0, 0, 0.35, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2.25, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -2.7}
## 381 {0, 0, 0, 1.036, 1.28242, 0, 0, 0, 0, 0, -2.333, 0, 0, 2.133, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 382 {0, 0, 0, 0, 0, 0, 0, 0, 0, -2.693, 0, 0, 0, 0, -0.814, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.9, 0, 0, 0, 0, -0.4, 0}
## 383 {0, 0, 0, 0, 0, 0, 0}
## 384 {0, 0, 0, -1.15, -1.75, -1.3, 0, -0.3, 0, 0, 0, 1.1, 0, 0, 0, 0, 2.85, 0, -4.65, 0, 0, 0, 0, 0, 0}
## 385 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 386 {0, 0, 0, 0, 0, 0, 0, 0, 0, 1.3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 387 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2.22, 0, 0, 0, 0, -1.8, 2.7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 388 {0, 0, 1.8, 0, 0, 0, 0, 0, -1.5, 0}
## 389 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -4.617525, 0, 0, 0}
## 390 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 391 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 392 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2.3, 0, 0, 0, 0, 0}
## 393 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 394 {0, 0, 0, 0, 0}
## 395 {0, 1.5, 0, 0, 0, 0, 0, 0, 0.8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 396 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 397 {0, 0, -1.4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.5, 0, 0, -2.8, 0}
## 398 {0, 0, 0, 0, 0, -2.3, 0}
## 399 {0, 0, 0, -2.1, 0, 0, 0, 0}
## 400 {0, 0, 0, 0, -1.933, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 401 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0.3, 0, -2.3, 0, 0, 0, 0, 0, 0, 0, 1.8, 0, 0, 0, 0, 0, 0, 0, -1.2, 0, 0, 0, 0}
## 402 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 403 {0, 0, 0, 0, 0, 0, -2.6, 0, 0, 0, 0, 1.3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.1, 0, 0, 0}
## 404 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.8, 0, 0, 0, -0.814, 0.37, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -3, 0, -3.2}
## 405 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -2.593}
## 406 {0, 1.5, 0, 0, 1.2, 0, 0, 0, 1.4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 407 {0, 0.9, 0, -1, 0, 0, 0, 0, 0, 0, -2.7, 0, 0, 0}
## 408 {0, 1.2, 0, 0, 0, 0, 0, -3.2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2.5, 0, 0, 0, 1.9, 0, 0}
## 409 {0, 0, 0, 0, 0, 0, 1.1, 0, 0, 0, 0, 0, 0, 0, -0.6465, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 410 {0, 0, 0, 0.2, 0, 0, 0, 0, 0, 0, 0, -2.793, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.593, 0, 0, 0, 0}
## 411 {0, 0, 0, 0}
## 412 {0, 0, 1.7, 0, 0, 0, 0, 0, 0, 1.7, 0, 0, 0, -2.1, 0, -2.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -2.2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.3, 0, 0, -1.933, 0, 0, 1.7, 0, 0, 0, 0, 0, -1.258, 0, 0}
## 413 {0, 0, 0, 0, 0, 0, 0, 0, 2.7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.3}
## 414 {0, 0, 0, -2.1, 0, 0, 0, 0}
## 415 {0, 0, 0, 0, 2.1, 0, 0, 0, 0}
## 416 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 417 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.3, 0, 0, 0, 0, 0, 0, -1.3, 0, 0, 0, -2.1, 0}
## 418 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 419 {0, -2.1, 0, 0, 0, 0, 0, 0, 0, -1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -3.4, 0}
## 420 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2.85, 0, 0, 0, 0, 0, 0, -1.95}
## 421 {1.9, 1.9, 0, 0, 0, 0, 0, 0, 0, -0.9, 0, 0, 0, 0, 0, 0, 0}
## 422 {0, 0, 0}
## 423 {0, 0, -0.814, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.7}
## 424 {0, 0, 0}
## 425 {0, 0, 0, 0, 0, 0, 0, 0, 0, 1.5, 0, 0, 0, 0, 0, 0, 0, -1.6, 0, 0, 0, 0, 0, 0, 0, 0, -2.3, 0, 0, 0, 0, 0, 0, 0, 0, -2.3, 0, 0, 0, 0, 0, 0}
## 426 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -2.6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 427 {0, 0, 0, 0, 0, 0, -1.1, 0, 0, 0, -2.6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2.3, 0, 0, 0, 0, 0, 1.9, 0, 0, 0, 0, 0, 1.9, 0}
## 428 {0, 0, 0, 0, 0, 0, 0, 0, 0, 1.2, 1.3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2.3, 0, 0, 0, 0, 0, 1.2, 1.8, 0, 0, 0, 0, 0, 0, 1.6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 429 {0, 0, 0, -0.2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 430 {0, 0, 0, 0, 0, 0, 0, 0, 2.35}
## 431 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -2.7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 432 {1.2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 433 {0, 0, 0, 0, 0, 0}
## 434 {0, 0, 0, 0, 0, 0, 0, 0, 0}
## 435 {0, 0, 1.8, 0, 0, 0, 0, 0, 3.2, 0, 0, 0, 0}
## 436 {0, 0, 0, 0, 0}
## 437 {0, 0, 0, 0, 0, 0, 0, 0, 1.1, 0}
## 438 {0, 0.7, 0, 0, 0, -0.6, 0, 0, 0, 0, 0, 0, 0, 2.25, 0, 0, 0, 0, 2.55, 0, 0, -3.45, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2.882475, 0, 0, 0, 0, 0, 0, 0}
## 439 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 440 {0, 0, 0}
## 441 {0, 0, 0, 0, 1.7, 0, 0, 0, 0, 0, 0, 0, 1.17835, 0, 0}
## 442 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -3.57835, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.2, 0, 0, 0, 0, 0}
## 443 {0, 0, 0, -2.1, 0, 0, 0, 0}
## 444 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 445 {0, 0, -1.11, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 446 {0, 0, 1.5, 0, 0, 0, 0, 0}
## 447 {0, 0, 0, 0, 0, 0, 1.3, 0, 0, 0, 0, 0.7, 0, 0, -1.8, 0, 0, 0, 0, 0, -2.55, 0, 0, 0, 0}
## 448 {0, 0, 0.95, 0, 0, 0, 0.8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 449 {0, 0, 0, 0, 0, 0}
## 450 {0, 0, 0, 0, 0, 0, 1.8, 0, 0, 0, 0, 0, 0, 0, 1.7, 0, 0, 0, 0, 0, 0, 0, 0, 0, -2.993, 0, 0, 0, 0, 0, 3.1, 0, 0, 0, 0, 0, 0, 0, 0}
## 451 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -2.3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.888, 0, 0, 0, -0.222, 0, 0, 0, 0}
## 452 {0, 2.35, 0, 0, 0, -1.7}
## 453 {0, 0, 0, 0, 0, 0, 0, 0, 0}
## 454 {0, 3.1, 0, 0, 0, 1.7, 0, 0, 0, 0, 0, 0, 1.7, 0, 0, 0, 0, 0, 0, 1.7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.2, 0, 0, 0, 0, 0, 0, 0, 0}
## 455 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -2.4, 0, 0, 0}
## 456 {0, 0, 0, 0, 0, 0, 0, 0, 0, 1.5}
## 457 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.6, 0}
## 458 {0, 0, 0, 0, 0, 0}
## 459 {0, 0, 0, 1.5, 0, 0, 0, 0}
## 460 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 461 {0, 0, 0, 0.85, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -2.85, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 462 {0, 0, 0}
## 463 {0, 0, 0, 0, 0, 3.1, 0, 0, 0, 0, 0, 0, 0, 0, 1.7, 0}
## 464 {0, 0, 0, 0, 0}
## 465 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 466 {0, 0, -2, 0, 0, 0, 0.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.5, 0}
## 467 {0, 0, 0, 0, 0, 0, 0}
## 468 {0, 0, 0, 0, 0, 0, -2.7, 0, 0, -2.1, 0, 0, 0, 0, 0, 0, -1.8, 0, 0, 0, -1.3, 0, 0, 0, 0, 0, 0, 0, 0, -2.2, 0, 0, -2.4, 0, 0, 0, -2.1, 0, 0, 0, 0, 0, 0, -2.2, 0}
## 469 {0, 0, 2.3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 470 {0, 0, 0, 0}
## 471 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2.633, 0, 0, 0, -2.7, 0, 1.3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 472 {0, 2.3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.1, 0}
## 473 {0, 0, 0, 0, 0, 0, 0, 0, 1.5, 0, 0}
## 474 {0, 0, 2.8, 0, -1.6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.9, 0, 0, 1.9, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 475 {0, 0, 0, 0, 0, 0, 0, 0, 1.3, 0, 0, 0, 0, 0, 0, 0, 0, 2.393, 0, 0, 0, 0, 0, 1.8, 0}
## 476 {0, 0, 0, 0, -1.628, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.5, -1.4, 0, -3.233, 0, 0, 0, 0, 0, 0, 0, 1.5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.11, 0}
## 477 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.6, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 478 {0, 0, -0.85, 0, 0, 0, 0, 0, 0, 0, -1.05, 0, -1.05, 0, 0.75, 0, 0, 0, 0, 0, 0, 0, 0, 0.9465, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.85, 0, 0, 0, 0, 0, 0, -1.443, 0, -2.109, 0, 0, 0, 0, 0}
## 479 {0, 0, 0, 0, 0, 0}
## 480 {0, 0, 0}
## 481 {0, 0, 0, -1.5, 0}
## 482 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 483 {0, 0, 0, 0, 0, 0, 0, 0}
## 484 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2.35, -0.4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 485 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 486 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2.6, 0, 0, 0, 0, 0, 0, 0, 0, 0, -2.7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.3, 0, 0, 0, 0, 0, 0}
## 487 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 488 {0, 0, 0, 0, 0.3, 0, 0, 0, 0, 0, 0, 0, -1.8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.2, 0, -1.3, 0, -1.8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 489 {0, 0, 0, 0.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -0.693, 0, 0}
## 490 {0, 0, 0, 0, 0, 0, 0, -0.7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2.85, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 491 {0, 0, 0, 1.5, 0, 0, 0, 0, 0, 0}
## 492 {0, 0, 0, 0, 3.233, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.666, 0, -2.072, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 493 {0, 0, 0, 0, 0, 0, 0}
## 494 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 495 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 496 {0, 0, 0, 0, 0, 0, 0, -1.7, 0, 0, 0, 1.4, 0, -1.7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 497 {0, 0, 0, 0, 0, 0, -3.1, 0, 0, 0, 0, 0, 0, -3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.296, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 498 {0, 0, 0, 0, 0, -0.703, 0, -0.2, 0, 0, 0, 0, 0, -0.2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.982475, 0, 0, 0, 0, 0, 0, -1.65}
## 499 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.9, 0, 0, 0, 0, 0, 0}
## 500 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.6, 0, 0}
## compound pos neu neg but_count
## 1 0.000 0.000 1.000 0.000 0
## 2 0.000 0.000 1.000 0.000 0
## 3 0.788 0.194 0.806 0.000 1
## 4 0.586 0.188 0.812 0.000 0
## 5 0.000 0.000 1.000 0.000 0
## 6 0.660 0.118 0.882 0.000 0
## 7 -0.178 0.059 0.858 0.083 0
## 8 0.000 0.000 1.000 0.000 0
## 9 -0.077 0.000 0.902 0.098 0
## 10 0.076 0.040 0.960 0.000 0
## 11 0.202 0.083 0.917 0.000 0
## 12 -0.670 0.000 0.471 0.529 0
## 13 0.000 0.000 1.000 0.000 0
## 14 0.000 0.000 1.000 0.000 0
## 15 0.402 0.278 0.722 0.000 0
## 16 0.000 0.000 1.000 0.000 0
## 17 -0.691 0.000 0.905 0.095 0
## 18 0.758 0.155 0.794 0.051 0
## 19 0.000 0.000 1.000 0.000 0
## 20 0.402 0.474 0.526 0.000 0
## 21 0.000 0.000 1.000 0.000 0
## 22 -0.511 0.000 0.602 0.398 0
## 23 0.593 0.155 0.845 0.000 1
## 24 0.000 0.000 1.000 0.000 0
## 25 0.000 0.000 1.000 0.000 0
## 26 -0.637 0.041 0.837 0.123 0
## 27 0.834 0.254 0.746 0.000 1
## 28 0.000 0.000 1.000 0.000 0
## 29 0.000 0.000 1.000 0.000 0
## 30 -0.361 0.000 0.848 0.152 0
## 31 0.000 0.000 1.000 0.000 0
## 32 -0.670 0.058 0.789 0.153 0
## 33 -0.625 0.000 0.796 0.204 0
## 34 0.000 0.000 1.000 0.000 0
## 35 0.273 0.139 0.861 0.000 0
## 36 0.000 0.000 1.000 0.000 0
## 37 0.000 0.000 1.000 0.000 0
## 38 0.000 0.000 1.000 0.000 0
## 39 -0.178 0.080 0.825 0.095 0
## 40 0.000 0.000 1.000 0.000 0
## 41 -0.681 0.000 0.635 0.365 1
## 42 -0.511 0.000 0.901 0.099 0
## 43 -0.402 0.000 0.787 0.213 0
## 44 0.000 0.000 1.000 0.000 0
## 45 0.273 0.116 0.884 0.000 0
## 46 0.746 0.232 0.655 0.113 2
## 47 0.809 0.195 0.729 0.077 1
## 48 0.273 0.189 0.811 0.000 0
## 49 -0.459 0.000 0.833 0.167 1
## 50 0.000 0.000 1.000 0.000 0
## 51 0.000 0.000 1.000 0.000 0
## 52 0.000 0.000 1.000 0.000 0
## 53 0.301 0.198 0.802 0.000 0
## 54 0.000 0.000 1.000 0.000 1
## 55 0.000 0.000 1.000 0.000 0
## 56 -0.641 0.088 0.666 0.246 0
## 57 -0.862 0.000 0.780 0.220 0
## 58 0.000 0.000 1.000 0.000 0
## 59 0.911 0.278 0.722 0.000 0
## 60 0.000 0.000 1.000 0.000 0
## 61 0.447 0.101 0.899 0.000 0
## 62 0.361 0.200 0.800 0.000 0
## 63 0.000 0.000 1.000 0.000 0
## 64 0.077 0.086 0.858 0.056 0
## 65 0.000 0.000 1.000 0.000 0
## 66 0.866 0.191 0.809 0.000 1
## 67 -0.663 0.000 0.875 0.125 0
## 68 0.000 0.000 1.000 0.000 0
## 69 -0.238 0.090 0.785 0.124 0
## 70 0.052 0.155 0.693 0.152 0
## 71 -0.727 0.000 0.736 0.264 0
## 72 -0.619 0.062 0.773 0.165 1
## 73 -0.361 0.000 0.737 0.263 0
## 74 0.296 0.145 0.855 0.000 0
## 75 -0.128 0.086 0.838 0.076 0
## 76 -0.612 0.164 0.547 0.289 0
## 77 -0.290 0.047 0.860 0.093 1
## 78 0.283 0.179 0.716 0.106 1
## 79 0.222 0.081 0.862 0.057 0
## 80 -0.718 0.000 0.854 0.146 0
## 81 0.523 0.184 0.816 0.000 0
## 82 -0.511 0.000 0.798 0.202 0
## 83 0.000 0.000 1.000 0.000 0
## 84 -0.718 0.000 0.857 0.143 0
## 85 0.128 0.111 0.765 0.124 0
## 86 0.822 0.261 0.677 0.062 0
## 87 0.000 0.000 1.000 0.000 0
## 88 0.000 0.000 1.000 0.000 0
## 89 0.778 0.315 0.685 0.000 0
## 90 0.000 0.000 1.000 0.000 0
## 91 0.778 0.167 0.833 0.000 0
## 92 0.000 0.000 1.000 0.000 0
## 93 0.000 0.000 1.000 0.000 0
## 94 0.000 0.000 1.000 0.000 0
## 95 0.511 0.248 0.752 0.000 0
## 96 0.000 0.000 1.000 0.000 0
## 97 0.000 0.000 1.000 0.000 0
## 98 -0.660 0.000 0.795 0.205 0
## 99 0.735 0.135 0.865 0.000 0
## 100 0.153 0.056 0.904 0.040 0
## 101 0.657 0.166 0.834 0.000 0
## 102 0.000 0.000 1.000 0.000 0
## 103 0.402 0.091 0.909 0.000 0
## 104 -0.476 0.065 0.826 0.109 1
## 105 -0.677 0.000 0.637 0.363 0
## 106 -0.178 0.089 0.788 0.123 0
## 107 -0.872 0.074 0.649 0.277 0
## 108 -0.730 0.124 0.539 0.337 0
## 109 -0.077 0.155 0.743 0.102 0
## 110 0.000 0.000 1.000 0.000 0
## 111 -0.536 0.000 0.918 0.082 0
## 112 0.000 0.000 1.000 0.000 0
## 113 0.000 0.116 0.767 0.116 0
## 114 -0.785 0.000 0.771 0.229 0
## 115 0.494 0.167 0.833 0.000 0
## 116 0.372 0.157 0.843 0.000 1
## 117 -0.516 0.000 0.783 0.217 0
## 118 -0.583 0.035 0.864 0.100 0
## 119 0.623 0.338 0.662 0.000 0
## 120 0.525 0.091 0.909 0.000 0
## 121 0.000 0.000 1.000 0.000 0
## 122 0.000 0.000 1.000 0.000 0
## 123 0.000 0.000 1.000 0.000 0
## 124 0.318 0.049 0.951 0.000 0
## 125 0.258 0.203 0.677 0.120 1
## 126 -0.625 0.000 0.745 0.255 0
## 127 0.000 0.000 1.000 0.000 0
## 128 0.542 0.153 0.797 0.050 0
## 129 0.577 0.137 0.863 0.000 0
## 130 0.382 0.203 0.671 0.126 0
## 131 -0.572 0.000 0.619 0.381 0
## 132 0.178 0.036 0.964 0.000 1
## 133 -0.542 0.000 0.788 0.212 0
## 134 0.757 0.481 0.519 0.000 0
## 135 0.402 0.145 0.796 0.059 1
## 136 0.000 0.000 1.000 0.000 0
## 137 0.000 0.000 1.000 0.000 0
## 138 0.832 0.172 0.786 0.042 0
## 139 0.214 0.078 0.863 0.058 0
## 140 0.000 0.000 1.000 0.000 0
## 141 -0.561 0.116 0.535 0.350 0
## 142 -0.727 0.000 0.681 0.319 0
## 143 0.000 0.000 1.000 0.000 0
## 144 0.000 0.000 1.000 0.000 0
## 145 -0.657 0.186 0.559 0.254 1
## 146 0.000 0.000 1.000 0.000 0
## 147 0.000 0.000 1.000 0.000 0
## 148 0.000 0.000 1.000 0.000 0
## 149 -0.115 0.028 0.934 0.038 1
## 150 0.250 0.043 0.957 0.000 0
## 151 0.755 0.204 0.736 0.060 0
## 152 0.361 0.217 0.783 0.000 0
## 153 0.000 0.000 1.000 0.000 0
## 154 -0.361 0.000 0.889 0.111 0
## 155 0.000 0.000 1.000 0.000 0
## 156 0.026 0.112 0.803 0.085 0
## 157 0.361 0.063 0.937 0.000 0
## 158 0.000 0.000 1.000 0.000 0
## 159 -0.738 0.000 0.764 0.236 0
## 160 0.250 0.062 0.938 0.000 0
## 161 -0.296 0.000 0.945 0.055 0
## 162 0.000 0.000 1.000 0.000 0
## 163 -0.394 0.092 0.767 0.141 0
## 164 0.000 0.000 1.000 0.000 0
## 165 -0.103 0.156 0.703 0.141 0
## 166 0.402 0.252 0.748 0.000 0
## 167 -0.572 0.000 0.831 0.169 0
## 168 0.666 0.206 0.794 0.000 0
## 169 0.294 0.225 0.625 0.150 0
## 170 -0.494 0.000 0.906 0.094 0
## 171 -0.691 0.000 0.865 0.135 0
## 172 0.386 0.064 0.906 0.030 2
## 173 0.000 0.000 1.000 0.000 0
## 174 0.000 0.000 1.000 0.000 0
## 175 0.844 0.378 0.622 0.000 0
## 176 0.361 0.091 0.909 0.000 0
## 177 -0.727 0.000 0.895 0.105 0
## 178 0.614 0.173 0.827 0.000 0
## 179 0.625 0.215 0.785 0.000 0
## 180 0.000 0.000 1.000 0.000 0
## 181 0.691 0.205 0.795 0.000 0
## 182 0.511 0.180 0.820 0.000 0
## 183 0.440 0.225 0.775 0.000 0
## 184 0.000 0.000 1.000 0.000 0
## 185 -0.542 0.000 0.588 0.412 0
## 186 0.807 0.235 0.716 0.049 0
## 187 0.250 0.080 0.920 0.000 0
## 188 0.000 0.000 1.000 0.000 0
## 189 0.727 0.196 0.733 0.071 0
## 190 0.852 0.197 0.803 0.000 1
## 191 0.612 0.088 0.912 0.000 0
## 192 0.402 0.137 0.863 0.000 0
## 193 0.735 0.137 0.863 0.000 0
## 194 0.519 0.251 0.749 0.000 0
## 195 0.000 0.000 1.000 0.000 0
## 196 0.637 0.106 0.894 0.000 0
## 197 -0.348 0.042 0.889 0.068 0
## 198 -0.421 0.000 0.851 0.149 0
## 199 0.000 0.000 1.000 0.000 0
## 200 0.000 0.000 1.000 0.000 0
## 201 0.000 0.000 1.000 0.000 0
## 202 0.000 0.000 1.000 0.000 0
## 203 -0.572 0.000 0.802 0.198 0
## 204 0.000 0.000 1.000 0.000 0
## 205 -0.585 0.000 0.594 0.406 0
## 206 0.000 0.000 1.000 0.000 0
## 207 0.439 0.109 0.838 0.053 0
## 208 -0.440 0.000 0.881 0.119 0
## 209 -0.128 0.092 0.769 0.138 0
## 210 0.000 0.000 1.000 0.000 0
## 211 0.709 0.228 0.772 0.000 0
## 212 -0.354 0.030 0.885 0.085 0
## 213 0.247 0.146 0.740 0.114 0
## 214 0.000 0.000 1.000 0.000 0
## 215 0.700 0.185 0.815 0.000 0
## 216 0.000 0.000 1.000 0.000 0
## 217 0.051 0.091 0.824 0.085 0
## 218 0.028 0.110 0.781 0.108 0
## 219 -0.701 0.000 0.721 0.279 0
## 220 -0.574 0.035 0.841 0.123 0
## 221 0.527 0.227 0.773 0.000 0
## 222 -0.733 0.000 0.619 0.381 0
## 223 0.000 0.000 1.000 0.000 0
## 224 -0.710 0.000 0.906 0.094 1
## 225 0.000 0.000 1.000 0.000 0
## 226 0.340 0.128 0.787 0.086 0
## 227 -0.696 0.000 0.759 0.241 1
## 228 0.000 0.000 1.000 0.000 0
## 229 0.511 0.125 0.875 0.000 0
## 230 0.440 0.091 0.909 0.000 0
## 231 0.898 0.306 0.694 0.000 0
## 232 0.474 0.063 0.937 0.000 0
## 233 0.000 0.000 1.000 0.000 0
## 234 0.185 0.060 0.897 0.043 1
## 235 0.586 0.129 0.817 0.053 1
## 236 0.000 0.000 1.000 0.000 0
## 237 0.000 0.000 1.000 0.000 0
## 238 0.000 0.000 1.000 0.000 0
## 239 -0.881 0.000 0.672 0.328 0
## 240 0.000 0.000 1.000 0.000 0
## 241 0.803 0.283 0.578 0.139 0
## 242 0.459 0.055 0.945 0.000 0
## 243 -0.489 0.000 0.881 0.119 0
## 244 -0.864 0.000 0.675 0.325 0
## 245 -0.710 0.100 0.720 0.180 1
## 246 0.000 0.000 1.000 0.000 0
## 247 -0.718 0.000 0.709 0.291 0
## 248 -0.511 0.000 0.798 0.202 0
## 249 0.440 0.420 0.580 0.000 0
## 250 0.000 0.000 1.000 0.000 0
## 251 0.000 0.000 1.000 0.000 0
## 252 -0.788 0.051 0.788 0.161 0
## 253 0.000 0.000 1.000 0.000 0
## 254 -0.542 0.000 0.462 0.538 0
## 255 0.511 0.123 0.829 0.048 0
## 256 -0.527 0.000 0.884 0.116 0
## 257 0.906 0.295 0.639 0.066 0
## 258 -0.891 0.048 0.703 0.249 0
## 259 0.178 0.058 0.904 0.038 0
## 260 0.273 0.096 0.853 0.051 0
## 261 -0.077 0.138 0.719 0.144 0
## 262 -0.188 0.000 0.882 0.118 0
## 263 0.000 0.000 1.000 0.000 0
## 264 0.660 0.188 0.747 0.066 0
## 265 0.026 0.088 0.826 0.086 0
## 266 0.000 0.000 1.000 0.000 0
## 267 0.000 0.000 1.000 0.000 0
## 268 0.457 0.107 0.893 0.000 0
## 269 -0.103 0.085 0.813 0.102 0
## 270 0.375 0.147 0.810 0.043 1
## 271 0.296 0.140 0.773 0.087 0
## 272 -0.815 0.000 0.858 0.142 0
## 273 -0.296 0.000 0.761 0.239 0
## 274 0.000 0.000 1.000 0.000 0
## 275 -0.318 0.000 0.827 0.173 0
## 276 -0.477 0.000 0.912 0.088 0
## 277 0.382 0.126 0.874 0.000 0
## 278 0.000 0.000 1.000 0.000 0
## 279 0.930 0.363 0.585 0.052 0
## 280 -0.710 0.064 0.713 0.223 0
## 281 0.223 0.176 0.696 0.128 0
## 282 -0.527 0.000 0.702 0.298 0
## 283 0.000 0.000 1.000 0.000 0
## 284 0.660 0.178 0.822 0.000 0
## 285 0.619 0.111 0.843 0.046 0
## 286 0.000 0.000 1.000 0.000 0
## 287 0.000 0.000 1.000 0.000 0
## 288 0.691 0.180 0.820 0.000 0
## 289 0.000 0.000 1.000 0.000 0
## 290 0.118 0.072 0.868 0.060 0
## 291 -0.296 0.000 0.833 0.167 0
## 292 0.000 0.000 1.000 0.000 0
## 293 0.000 0.000 1.000 0.000 0
## 294 -0.785 0.000 0.846 0.154 0
## 295 0.296 0.071 0.929 0.000 0
## 296 0.128 0.201 0.648 0.150 0
## 297 -0.273 0.000 0.951 0.049 0
## 298 0.402 0.184 0.816 0.000 0
## 299 -0.599 0.000 0.698 0.302 0
## 300 0.000 0.000 1.000 0.000 0
## 301 -0.318 0.061 0.828 0.111 0
## 302 -0.579 0.090 0.752 0.159 1
## 303 -0.617 0.163 0.598 0.239 0
## 304 -0.778 0.000 0.762 0.238 0
## 305 0.000 0.000 1.000 0.000 0
## 306 0.542 0.099 0.901 0.000 0
## 307 -0.625 0.000 0.796 0.204 0
## 308 -0.509 0.000 0.935 0.065 0
## 309 -0.276 0.000 0.936 0.064 0
## 310 0.733 0.170 0.797 0.032 0
## 311 0.000 0.000 1.000 0.000 0
## 312 0.000 0.000 1.000 0.000 0
## 313 0.440 0.062 0.938 0.000 0
## 314 -0.178 0.128 0.711 0.161 0
## 315 0.000 0.000 1.000 0.000 0
## 316 0.660 0.178 0.822 0.000 0
## 317 0.000 0.000 1.000 0.000 0
## 318 0.889 0.333 0.667 0.000 0
## 319 0.283 0.128 0.797 0.075 1
## 320 -0.872 0.000 0.801 0.199 0
## 321 0.000 0.000 1.000 0.000 0
## 322 -0.848 0.000 0.801 0.199 0
## 323 -0.934 0.000 0.694 0.306 0
## 324 0.000 0.000 1.000 0.000 0
## 325 0.402 0.058 0.942 0.000 0
## 326 0.000 0.000 1.000 0.000 0
## 327 0.000 0.000 1.000 0.000 0
## 328 -0.690 0.000 0.657 0.343 0
## 329 0.000 0.000 1.000 0.000 0
## 330 0.771 0.235 0.765 0.000 1
## 331 -0.296 0.000 0.885 0.115 0
## 332 0.700 0.239 0.690 0.072 0
## 333 0.741 0.146 0.812 0.042 0
## 334 0.000 0.000 1.000 0.000 0
## 335 0.000 0.000 1.000 0.000 0
## 336 -0.465 0.000 0.924 0.076 0
## 337 0.000 0.000 1.000 0.000 0
## 338 0.000 0.000 1.000 0.000 0
## 339 -0.329 0.053 0.844 0.103 1
## 340 0.000 0.000 1.000 0.000 0
## 341 -0.296 0.000 0.872 0.128 0
## 342 0.000 0.000 1.000 0.000 0
## 343 0.000 0.000 1.000 0.000 0
## 344 0.000 0.000 1.000 0.000 0
## 345 -0.103 0.000 0.903 0.097 0
## 346 0.000 0.000 1.000 0.000 0
## 347 0.000 0.000 1.000 0.000 0
## 348 0.318 0.134 0.801 0.065 0
## 349 0.791 0.290 0.710 0.000 0
## 350 0.660 0.435 0.565 0.000 0
## 351 0.000 0.000 1.000 0.000 0
## 352 0.000 0.000 1.000 0.000 0
## 353 0.178 0.057 0.900 0.043 0
## 354 0.361 0.161 0.839 0.000 0
## 355 -0.463 0.193 0.548 0.259 0
## 356 0.000 0.000 1.000 0.000 0
## 357 0.000 0.000 1.000 0.000 0
## 358 -0.586 0.000 0.826 0.174 0
## 359 0.000 0.000 1.000 0.000 0
## 360 0.000 0.000 1.000 0.000 0
## 361 0.000 0.000 1.000 0.000 0
## 362 0.475 0.162 0.739 0.099 0
## 363 0.000 0.000 1.000 0.000 0
## 364 0.153 0.143 0.724 0.133 0
## 365 0.000 0.000 1.000 0.000 0
## 366 -0.830 0.000 0.586 0.414 0
## 367 -0.710 0.095 0.700 0.205 0
## 368 0.402 0.252 0.748 0.000 0
## 369 -0.742 0.000 0.769 0.231 0
## 370 -0.391 0.026 0.885 0.089 1
## 371 -0.047 0.091 0.835 0.075 1
## 372 0.340 0.245 0.625 0.130 0
## 373 0.361 0.111 0.889 0.000 0
## 374 0.018 0.136 0.688 0.176 0
## 375 0.000 0.000 1.000 0.000 0
## 376 -0.511 0.000 0.719 0.281 0
## 377 -0.813 0.080 0.742 0.177 1
## 378 -0.660 0.000 0.899 0.101 0
## 379 -0.945 0.000 0.576 0.424 0
## 380 -0.026 0.082 0.853 0.066 1
## 381 0.480 0.183 0.736 0.082 0
## 382 -0.460 0.069 0.765 0.165 0
## 383 0.000 0.000 1.000 0.000 0
## 384 -0.884 0.148 0.447 0.405 1
## 385 0.000 0.000 1.000 0.000 0
## 386 0.599 0.129 0.871 0.000 0
## 387 0.627 0.134 0.812 0.054 1
## 388 0.077 0.211 0.602 0.188 0
## 389 -0.766 0.000 0.804 0.196 1
## 390 0.000 0.000 1.000 0.000 0
## 391 0.000 0.000 1.000 0.000 0
## 392 0.556 0.107 0.893 0.000 0
## 393 0.361 0.049 0.951 0.000 0
## 394 0.000 0.000 1.000 0.000 0
## 395 0.511 0.170 0.830 0.000 0
## 396 0.000 0.000 1.000 0.000 1
## 397 -0.572 0.094 0.674 0.232 0
## 398 -0.511 0.000 0.645 0.355 0
## 399 -0.477 0.000 0.693 0.307 0
## 400 -0.447 0.000 0.853 0.147 0
## 401 -0.340 0.109 0.745 0.146 0
## 402 0.000 0.000 1.000 0.000 0
## 403 -0.052 0.147 0.733 0.120 0
## 404 -0.928 0.023 0.724 0.253 0
## 405 -0.556 0.000 0.879 0.121 0
## 406 0.727 0.272 0.728 0.000 0
## 407 -0.586 0.102 0.591 0.306 0
## 408 0.527 0.216 0.678 0.106 0
## 409 0.116 0.046 0.918 0.036 1
## 410 -0.734 0.032 0.798 0.170 0
## 411 0.000 0.000 1.000 0.000 0
## 412 -0.669 0.148 0.640 0.212 0
## 413 0.507 0.102 0.847 0.051 0
## 414 -0.477 0.000 0.693 0.307 0
## 415 0.477 0.279 0.721 0.000 0
## 416 -0.296 0.000 0.945 0.055 0
## 417 -0.772 0.000 0.918 0.082 0
## 418 0.000 0.000 1.000 0.000 0
## 419 -0.859 0.000 0.667 0.333 0
## 420 0.226 0.149 0.736 0.114 2
## 421 0.599 0.267 0.645 0.088 0
## 422 0.000 0.000 1.000 0.000 0
## 423 -0.544 0.000 0.756 0.244 0
## 424 0.000 0.000 1.000 0.000 0
## 425 -0.772 0.050 0.765 0.185 0
## 426 -0.557 0.000 0.896 0.104 0
## 427 0.527 0.183 0.703 0.114 0
## 428 0.929 0.287 0.713 0.000 0
## 429 -0.052 0.000 0.934 0.066 0
## 430 0.519 0.295 0.705 0.000 0
## 431 -0.572 0.000 0.913 0.087 1
## 432 0.359 0.060 0.940 0.000 0
## 433 0.000 0.000 1.000 0.000 0
## 434 0.000 0.000 1.000 0.000 0
## 435 0.791 0.389 0.611 0.000 0
## 436 0.000 0.000 1.000 0.000 0
## 437 0.273 0.189 0.811 0.000 0
## 438 0.746 0.232 0.655 0.113 2
## 439 0.000 0.000 1.000 0.000 0
## 440 0.000 0.000 1.000 0.000 0
## 441 0.596 0.273 0.727 0.000 0
## 442 -0.777 0.000 0.852 0.148 0
## 443 -0.477 0.000 0.693 0.307 0
## 444 0.000 0.000 1.000 0.000 0
## 445 0.151 0.070 0.876 0.054 0
## 446 0.361 0.263 0.737 0.000 0
## 447 -0.519 0.128 0.670 0.203 1
## 448 0.466 0.175 0.825 0.000 1
## 449 0.000 0.000 1.000 0.000 0
## 450 0.682 0.198 0.720 0.082 0
## 451 -0.445 0.039 0.862 0.099 0
## 452 0.166 0.333 0.398 0.269 0
## 453 0.000 0.000 1.000 0.000 0
## 454 0.932 0.294 0.706 0.000 0
## 455 -0.178 0.050 0.887 0.063 0
## 456 0.361 0.217 0.783 0.000 0
## 457 0.382 0.167 0.833 0.000 0
## 458 0.000 0.000 1.000 0.000 0
## 459 0.420 0.285 0.715 0.000 0
## 460 0.000 0.000 1.000 0.000 0
## 461 -0.459 0.050 0.845 0.105 1
## 462 0.000 0.000 1.000 0.000 0
## 463 0.778 0.327 0.673 0.000 0
## 464 0.000 0.000 1.000 0.000 0
## 465 0.000 0.000 1.000 0.000 0
## 466 0.337 0.154 0.773 0.072 0
## 467 0.000 0.000 1.000 0.000 0
## 468 -0.974 0.000 0.599 0.401 0
## 469 0.511 0.216 0.784 0.000 0
## 470 0.000 0.000 1.000 0.000 0
## 471 0.655 0.198 0.711 0.091 0
## 472 0.660 0.178 0.822 0.000 0
## 473 0.361 0.200 0.800 0.000 0
## 474 0.791 0.251 0.681 0.068 0
## 475 0.817 0.279 0.721 0.000 0
## 476 -0.748 0.095 0.687 0.217 0
## 477 -0.525 0.042 0.855 0.104 0
## 478 -0.739 0.093 0.708 0.199 1
## 479 0.000 0.000 1.000 0.000 0
## 480 0.000 0.000 1.000 0.000 0
## 481 -0.361 0.000 0.615 0.385 0
## 482 0.000 0.000 1.000 0.000 0
## 483 0.000 0.000 1.000 0.000 0
## 484 0.450 0.086 0.877 0.036 0
## 485 0.000 0.000 1.000 0.000 1
## 486 0.000 0.106 0.789 0.106 0
## 487 0.000 0.000 1.000 0.000 0
## 488 -0.844 0.027 0.760 0.213 0
## 489 -0.239 0.044 0.875 0.082 0
## 490 0.533 0.078 0.889 0.032 1
## 491 0.361 0.217 0.783 0.000 0
## 492 0.480 0.121 0.819 0.060 0
## 493 0.000 0.000 1.000 0.000 0
## 494 0.000 0.000 1.000 0.000 0
## 495 0.000 0.000 1.000 0.000 0
## 496 -0.459 0.083 0.729 0.188 0
## 497 -0.832 0.025 0.817 0.158 0
## 498 -0.774 0.000 0.782 0.218 2
## 499 0.637 0.113 0.887 0.000 0
## 500 -0.586 0.000 0.893 0.107 0Take a look at vader_summary data frame using the View() function in the console and sort by most positive and negative tweets.
Does it generally seem accurately identify positive and negative tweets? Could you find any that you think were mislabeled?
- Generally they are classified appropriately. I did find 1 instance (#290) that was labeled positive but it’s sarcasm and is really negative.
Hutto, C. & Gilbert, E. (2014) provide an excellent summary of the VADER package on their GitHub repository and I’ve copied and explanation of the scores below:
- The
compoundscore is computed by summing the valence scores of each word in the lexicon, adjusted according to the rules, and then normalized to be between -1 (most extreme negative) and +1 (most extreme positive). This is the most useful metric if you want a single unidimensional measure of sentiment for a given sentence. Calling it a ‘normalized, weighted composite score’ is accurate.
NOTE: The compound score is the one most commonly used for sentiment analysis by most researchers, including the authors.
Let’s take a look at the average compound score for our CCSS sample of tweets:
mean(vader_ccss$compound)## [1] -2.2e-05Overall, does your CCSS tweets sample lean slightly negative or positive? Is this what you expected?
What if we wanted to compare these results more easily to our other sentiment lexicons just to check if result are fairly consistent?
The author’s note that it is also useful for researchers who would like to set standardized thresholds for classifying sentences as either positive, neutral, or negative. Typical threshold values are:
positive sentiment:
compoundscore >= 0.05neutral sentiment: (
compoundscore > -0.05) and (compoundscore < 0.05)negative sentiment:
compoundscore <= -0.05
Let’s give that a try and see how things shake out:
vader_ccss_summary <- vader_ccss %>%
mutate(sentiment = ifelse(compound >= 0.05, "positive",
ifelse(compound <= -0.05, "negative", "neutral"))) %>%
count(sentiment, sort = TRUE) %>%
spread(sentiment, n) %>%
relocate(positive) %>%
mutate(ratio = negative/positive)
vader_ccss_summary## positive negative neutral ratio
## 1 172 157 171 0.9127907Not quite as bleak as we might have expected according to VADER! But then again, VADER brings an entirely different perspective coming from the dark side
Your Turn ⤵
In a separate R script file, try using VADER to perform a sentiment analysis of the NGSS tweets and see how they compare. Post your working code in the chunk below and answer the questions that follow:
# Create 500-instance sample of NGSS dataset
ngss_sample <- read_csv(here("unit-3", "data", "ngss-tweets.csv")) %>%
sample_n(500)## Warning: One or more parsing issues, see `problems()` for details## Rows: 8126 Columns: 8## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (2): text, source
## dbl (4): author_id, id, conversation_id, in_reply_to_user_id
## lgl (1): possibly_sensitive
## dttm (1): created_at##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.# Vader the NGSS sample
vader_ngss <- vader_df(ngss_sample$text)
# Compute average compound sentiment of NGSS tweets
mean(vader_ngss$compound)## [1] 0.427874# Summarize Vader Sentiment Analysis
vader_ngss_summary <- vader_ngss %>%
mutate(sentiment = ifelse(compound >= 0.05, "positive",
ifelse(compound <= -0.05, "negative", "neutral"))) %>%
count(sentiment, sort = TRUE) %>%
spread(sentiment, n) %>%
relocate(positive) %>%
mutate(ratio = negative/positive)
vader_ngss_summary## positive negative neutral ratio
## 1 356 34 110 0.09550562How do our results compare to the CSSS sample of tweets?
- Much more uniformly positive. Ratio was 0.13.
How do these result compare to those found by Rosenberg et al. (2021)?
- My results were slightly less positive has his findings resulted in a ratio of 0.11, but still relatively close. I expect the difference was due to my sample size.
5. COMMUNICATE
In this case study, we focused on the literature guiding our analysis; wrangling our data into a one-token-per-row tidy text format; using simple word counts and word clouds to explore our data; and comparing sentiment in tweets about the NGSS and CCSS curriculum standards.
In response to the following research questions driving this analysis, write a 2-3 sentences summarizing our findings:
What are the most frequent words or phrases used in reference to tweets about the CCSS and NGSS?
Math, education, and standards were the most common terms in the CCSS tweets.
Science, students, and learning were the big 3 in the NGSS tweets.
The most common terms were all single words as we used unigrams as opposed to any groupings for phrases.
How does sentiment for NGSS compare to sentiment for CCSS?
- NGSS tweet sentiment was overwhelmingly positive (by a margin of almost 10:1), while the CCSS tweets were more neutrally balanced.
Finally, add 1-2 sentences in response to the following prompts:
One important thing I took away from this case study about text mining :
- How you tidy the data has a huge impact on the types of insights you can draw. Having a good plan on how to tidy based on your research questions is key to your success.
One thing about text mining I want to learn more about:
- Are there text mining dictionaries based on mis/disinformation? Or this that too context-dependent? In other words, can we use similar methods to determine if a group of words is true or false?
Congratulations - you’ve completed your first text mining case study! To complete your work, you can click the drop down arrow at the top of the file, then select “Knit top HTML.” This will create a report in your Files pane that serves as a record of your code and its output you can open or share.
Extra Credit (Optional)
Sentiment Analysis
In a separate R script file, and using your own text data or data that you you pulled from Twitter above, try tidying your data into a tidy text format, examining the top words in your dataset, and conducting sentiment analysis with VADER. Post your final code here:
# Import news articles from real and fake source files
news_real <- read_csv(here("unit-3", "data", "news_real.csv"),
show_col_types = FALSE)
news_fake <- read_csv(here("unit-3", "data", "news_fake.csv"),
show_col_types = FALSE)
# Relocate class column to first position
news_real_1 <- relocate(news_real, class)
news_fake_1 <- relocate(news_fake, class)
# Reduce dataframe size to a randome 450 instances for simpler analysis
news_real_2 <- news_real_1[sample(1:nrow(news_real_1), 450), ]
news_fake_2 <- news_fake_1[sample(1:nrow(news_fake_1), 450), ]
# Merge real and fake dataframes
news <- union(news_real_2,
news_fake_2)
# Tokenize news article text
news_tokens <- news %>%
unnest_tokens(output = word,
input = text) %>%
relocate(word)
# Remove stop words
news_tokens_1 <- anti_join(news_tokens,
stop_words,
by = "word")
# Save tidy news data as .csv
news_tidy <- news_tokens_1
write_csv(news_tokens_1, here("unit-3", "data", "news_tidy.csv"))
# Measure sentiment of fake news stories
vader_fake <- vader_df(news_fake_2$text)
# Summarize fake news sentiment analysis
vader_fake_summary <- vader_fake %>%
mutate(sentiment = ifelse(compound >= 0.05, "positive",
ifelse(compound <= -0.05, "negative", "neutral"))) %>%
count(sentiment, sort = TRUE) %>%
spread(sentiment, n) %>%
relocate(positive) %>%
mutate(ratio = negative/positive)
# Measure sentiment of real news stories
vader_real <- vader_df(news_real_2$text)
# Summarize real news sentiment analysis
vader_real_summary <- vader_real %>%
mutate(sentiment = ifelse(compound >= 0.05, "positive",
ifelse(compound <= -0.05, "negative", "neutral"))) %>%
count(sentiment, sort = TRUE) %>%
spread(sentiment, n) %>%
relocate(positive) %>%
mutate(ratio = negative/positive)# Review top 50 tokens for fake news
fake_top_tokens <- news_tidy %>%
filter(class == "Fake") %>%
count(word, sort = TRUE) %>%
top_n(50)## Selecting by n# Create word cloud for fake news
wordcloud2(fake_top_tokens)# Review top 50 tokens for real news
real_top_tokens <- news_tidy %>%
filter(class == "Real") %>%
count(word, sort = TRUE) %>%
top_n(50)## Selecting by n# Create word cloud for fake news, filter for unnecessary words
real_top_tokens %>%
filter(word != "reuters" & word != "u.s") %>%
wordcloud2()vader_fake_summary## positive negative neutral ratio
## 1 195 251 4 1.287179vader_real_summary## positive negative neutral <NA> ratio
## 1 99 115 4 232 1.161616In general, news was fairly negative around the 2016 elections. That said, fake news trended slightly less negative than did the real news articles.
Exploring Bigrams
If you’d like to use the data we’ve been working with for extra credit, let’s take a quick look at text analysis using bigrams, or tokens consisting of two words.
So far in this lab, we specified tokens as individual words, but many interesting text analyses are based on the relationships between words, which words tend to follow others immediately, or words that tend to co-occur within the same documents.
We can also use the unnest_tokens() function to tokenize our tweets into consecutive sequences of words, called n-grams. By seeing how often word X is followed by word Y, we could then build a model of the relationships between them.
To specify our tokens as bigrams, We do add token = "ngrams" to the unnest_tokens() function and setting n to the number of words in each n-gram. Let’s set n to 2, so we can examine pairs of two consecutive words, often called “bigrams”:
ngss_bigrams <- ngss_tweets %>%
unnest_tokens(bigram,
text,
token = "ngrams",
n = 2)Before we move any further let’s take a quick look at the most common bigrams in our NGSS tweets:
ngss_bigrams %>%
count(bigram, sort = TRUE)## # A tibble: 111,411 × 2
## bigram n
## <chr> <int>
## 1 https t.co 6240
## 2 ngsschat https 721
## 3 of the 630
## 4 in the 531
## 5 ngss https 455
## 6 the ngss 403
## 7 to the 318
## 8 for the 295
## 9 to be 272
## 10 on the 239
## # … with 111,401 more rowsAs we saw above, a lot of the most common bigrams are pairs of common (uninteresting) words as well. Dealing with these is a little less straightforward and we’ll need to use the separate() function from the tidyr package, which splits a column into multiple based on a delimiter. This lets us separate it into two columns, “word1” and “word2,” at which point we can remove cases where either is a stop-word.
library(tidyr)
bigrams_separated <- ngss_bigrams %>%
separate(bigram, c("word1", "word2"), sep = " ")
bigrams_filtered <- bigrams_separated %>%
filter(!word1 %in% stop_words$word) %>%
filter(!word2 %in% stop_words$word)
tidy_bigrams <- bigrams_filtered %>%
unite(bigram, word1, word2, sep = " ")Let’s take a look at our bigram counts now:
tidy_bigrams %>%
count(bigram, sort = TRUE)## # A tibble: 45,507 × 2
## bigram n
## <chr> <int>
## 1 https t.co 6240
## 2 ngsschat https 721
## 3 ngss https 455
## 4 ngss ngsschat 236
## 5 ngss aligned 192
## 6 ngss standards 168
## 7 ngss science 154
## 8 science education 148
## 9 science standards 112
## 10 teachers https 106
## # … with 45,497 more rowsBetter, but there are still many tokens not especially useful for analysis.
Let’s make a custom custom stop word dictionary for bigrams just like we did for our unigrams. A list is started for you below, but you likely want to expand our list off stop words:
my_words <- c("https", "t.co", "ngss_tweeps")Now let’s separate, filter, and unite again:
tidy_bigrams <- bigrams_separated %>%
filter(!word1 %in% stop_words$word) %>%
filter(!word2 %in% stop_words$word) %>%
filter(!word1 %in% my_words) %>%
filter(!word2 %in% my_words) %>%
unite(bigram, word1, word2, sep = " ")Note that since my_words is just a vector of words and not a data frame like stop_words, we do not need to select the word column using the $ operator.
Let’s take another quick count of our bigrams:
tidy_bigrams %>%
count(bigram, sort = TRUE)## # A tibble: 36,934 × 2
## bigram n
## <chr> <int>
## 1 ngss ngsschat 236
## 2 ngss aligned 192
## 3 ngss standards 168
## 4 ngss science 154
## 5 science education 148
## 6 science standards 112
## 7 science ngss 94
## 8 approved approach 89
## 9 kid approved 89
## 10 science teachers 84
## # … with 36,924 more rowsYour Turn ⤵
Use the code chunk below to tidy and count our bigrams for the CCSS tweets:
ccss_bigrams <- ccss_tweets %>%
unnest_tokens(bigram,
text,
token = "ngrams",
n = 2)
ccss_bigrams %>%
count(bigram, sort = TRUE)## # A tibble: 251,343 × 2
## bigram n
## <chr> <int>
## 1 common core 26735
## 2 https t.co 9268
## 3 core math 8249
## 4 the common 2050
## 5 of the 1467
## 6 in the 1220
## 7 of common 1131
## 8 that common 1028
## 9 this is 980
## 10 core is 962
## # … with 251,333 more rowsccss_bigrams_separated <- ccss_bigrams %>%
separate(bigram, c("word1", "word2"), sep = " ")
ccss_bigrams_filtered <- ccss_bigrams_separated %>%
filter(!word1 %in% stop_words$word) %>%
filter(!word2 %in% stop_words$word)
ccss_tidy_bigrams <- ccss_bigrams_filtered %>%
unite(bigram, word1, word2, sep = " ")my_words <- c("https", "t.co", "common", "core")ccss_tidy_bigrams <- ccss_bigrams_separated %>%
filter(!word1 %in% stop_words$word) %>%
filter(!word2 %in% stop_words$word) %>%
filter(!word1 %in% my_words) %>%
filter(!word2 %in% my_words) %>%
unite(bigram, word1, word2, sep = " ")
ccss_tidy_bigrams %>%
count(bigram, sort = TRUE)## # A tibble: 78,953 × 2
## bigram n
## <chr> <int>
## 1 gt gt 262
## 2 bill gates 252
## 3 public schools 246
## 4 grade level 233
## 5 2 2 202
## 6 education system 194
## 7 critical thinking 191
## 8 commoncore technology 188
## 9 originally designed 188
## 10 argo brothers 185
## # … with 78,943 more rowsWhat additional insight, if any, did looking at bigrams bring to out analysis?
- You can identify specific personalities (Bill Gates, Argo Brothers) that could be involved with Common Core issues. You also see other issues arise due to 2-word context such as “public schools, critical thinking, and commoncore technology.
References
Note: Citations embedded in R Markdown will only show upon knitting.
Krumm, A., Means, B., & Bienkowski, M. (2018). Learning analytics goes to school. Routledge. https://doi.org/10.4324/9781315650722
Rosenberg, J. M., Borchers, C., Dyer, E. B., Anderson, D., & Fischer, C. (2021). Understanding Public Sentiment About Educational Reforms: The Next Generation Science Standards on Twitter. AERA Open, 7, 233285842110242. https://doi.org/10.1177/23328584211024261
Silge, J., & Robinson, D. (2017). Text mining with r: A tidy approach. " O’Reilly Media, Inc.". https://www.tidytextmining.com
Wang, Y., & Fikis, D. J. (2017). Common Core State Standards on Twitter: Public Sentiment and Opinion Leaders. Educational Policy, 33(4), 650–683. https://doi.org/10.1177/0895904817723739
---
title: 'Public Sentiment and the State Standards'
subtitle: 'Unit 3 Case Study'
author: "Dr. Shaun Kellogg"
date: "`r format(Sys.Date(),'%B %e, %Y')`"
output:
  html_document:
    toc: true
    toc_depth: 5
    toc_float: yes
    code_folding: show
    code_download: TRUE
bibliography: lit/references.bib
csl: lit/apa.csl
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```

## 1. PREPARE

Data sources such as digital learning environments and administrative data systems, as well as data produced by social media websites and the mass digitization of academic and practitioner publications, hold enormous potential to address a range of pressing problems in education, but collecting and analyzing text-based data also presents unique challenges. This week, our case study is guided by my colleague Josh Rosenberg's recent article, **Advancing new methods for understanding public sentiment about educational reforms: The case of Twitter and the Next Generation Science Standards.**

We will focus on conducting a very simplistic "replication study" by comparing the sentiment of tweets about the [Next Generation Science Standards](https://www.nextgenscience.org) (NGSS) and [Common Core State Standards](http://www.corestandards.org) (CCSS) in order to better understand public reaction to these two curriculum reform efforts. Specifically, our Unit 3 case study will cover the following topics:

1.  **Prepare**: We'll take a quick look at Dr. Rosenberg's study, load packages we'll need for analysis, and learn how to use Twitter's API to retrieve data for those that created a Twitter Developer account.
2.  **Wrangle**: We focus on basic text mining processes such as text tokenization and stop word removal. Specifically, we will learn how to "tidy text" so we can perform some basic analyses such as retrieving word counts and term frequencies.
3.  **Explore**: In order to see what insight our data provides into answering our research questions, we will calculate some basic simple summary statistics from our tidied text and use data visualization to highlight some of these insights.
4.  **Model:** We learn about sentiment lexicons in our third lab and introduce the {vader} package to model the sentiment of tweets about the NGSS and CCSS state standards in order to better understand public reaction to these two curriculum reform efforts.\
    **Communicate:**

### 1a. Review the Literature

The **Unit 3 Case Study: Public Sentiment and the State Standards** is guided by a recent publication by [@rosenberg2021] *Understanding Public Sentiment About Educational Reforms: The Next Generation Science Standards on Twitter*. This study in turn builds on upon previous work by @wang2017 examining public opinion on the Common Core State Standards (CCSS) on Twitter. For Module 1, we will focus on analyzing tweets about the [Next Generation Science Standards](https://www.nextgenscience.org) (NGSS) and [Common Core State Standards](http://www.corestandards.org) (CCSS) in order to better understand key words and phrases that emerge, as well as public sentiment towards these two curriculum reform efforts.

[![](img/rosenberg.jpg){width="50%"}](https://doi.org/10.1177/23328584211024261)

[Full Paper (AERA Open)](https://journals.sagepub.com/doi/full/10.1177/23328584211024261)

#### Abstract

System-wide educational reforms are difficult to implement in the United States, but despite the difficulties, reforms can be successful, particularly when they are associated with broad public support. This study reports on the nature of the public sentiment expressed about a nationwide science education reform effort, the Next Generation Science Standards (NGSS). Through the use of data science techniques to measure the sentiment of posts on Twitter about the NGSS (*N* = 565,283), we found that public sentiment about the NGSS is positive, with only 11 negative posts for every 100 positive posts. In contrast to findings from past research and public opinion polling on the Common Core State Standards, sentiment about the NGSS has become more positive over time---and was especially positive for teachers. We discuss what this positive sentiment may indicate about the success of the NGSS in light of opposition to the Common Core State Standards.

#### Data Sources

Similar to data we'll be using for this case study, Rosenberg et al. used publicly accessible data from Twitter collected using the Full-Archive Twitter API and the {rtweet} package in R. Specifically, the authors accessed tweets and user information from the hashtag-based #NGSSchat online community, all tweets that included any of the following phrases, with "/" indicating an additional phrase featuring the respective plural form: "ngss", "next generation science standard/s", "next gen science standard/s".

Data used in this case was pulled using an [Academic Research developer account](https://developer.twitter.com/en/products/twitter-api/academic-research) and the {academictwitter} package, which uses the Twitter API v2 endpoints and allows researchers to access the full twitter archive, unlike the standard developer account. Data includes all tweets from January through May of 2020 and included the following terms: `#ccss`, `common core`, `#ngsschat`, `ngss`.

Below is an example of the code used to retrieve data for this lab. **This code is set not to execute and will not run**, but it does illustrate the search query used, variables selected, and time frame. For those that created a standard developer account, we will learn how to use your developer account later in this section to retrieve data from Twitter.

```{r, eval=FALSE}
library(academictwitteR)
library(tidyverse)

ccss_tweets_2021 <-
  get_all_tweets('(#commoncore OR "common core") -is:retweet lang:en',
                 "2021-01-01T00:00:00Z",
                 "2021-05-31T00:00:00Z",
                 bearer_token,
                 data_path = "ccss-data/",
                 bind_tweets = FALSE)

ccss_tweets <- bind_tweet_jsons(data_path = "ccss-data/") %>%
  select(text,
         created_at,
         author_id,
         id,
         conversation_id,
         source,
         possibly_sensitive,
         in_reply_to_user_id)


write_csv(ccss_tweets, here("data", "ccss-tweets.csv"))
```

#### Analysis

The authors determined Tweet sentiment using the Java version of SentiStrength to assign tweets to two 5-point scales of sentiment, one for positivity and one for negativity, because SentiStrength is a validated measure for sentiment in short informal texts (Thelwall et al., 2011). In addition, they used this tool because Wang and Fikis (2019) used it to explore the sentiment of CCSS-related posts. We'll be using the AFINN sentiment lexicon which also assigns words in a tweet to two 5-point scales, in addition to exploring some other sentiment lexicons to see if they produce similar results. We will use a similar approach to label tweets as positive, negative, or neutral using the {Vader} package which greatly simplifies this process.

The authors also used the `lme4` package in R to run a mixed effects model to determine if sentiment changes over time and differs between teachers and non-teachers. We won't try to replicate in this study, but we will take a look at some of their findings from this model in below.

#### **Summary of Key Findings**

1.  Contrasting with sentiment about CSSS, sentiment about the NGSS science education reform effort is overwhelmingly positive, with approximately 9 positive tweets for every negative tweet.
2.  Teachers were more positive than non-teachers, and sentiment became substantially more positive over the ten years of NGSS-related posts.
3.  Differences between the context of the tweets were small, but those that did not include the #NGSSchat hashtag became more positive over time than those posts that did not include the hashtag.
4.  Individuals posted more tweets during #NGSSchat chats, the sentiment of their posts was more positive, suggesting that while the context of individual tweets has a small effect (with posts not including the hashtag becoming more positive over time), the effect upon individuals of being involved in the #NGSSchat was positive.

Finally, you can watch Dr. Rosenberg provide a quick 3-minute overview of this work at [\<https://stanford.app.box.com/s/i5ixkj2b8dyy8q5j9o5ww4nafznb497x>](https://stanford.app.box.com/s/i5ixkj2b8dyy8q5j9o5ww4nafznb497x){.uri}

### 1b. Define Questions

One overarching question that Silge and Robinson (2018) identify as a central question to text mining and natural language processing, and that we'll explore later in this case study, is the question:

> How do we to **quantify** what a document or collection of documents is about?

The questions guiding the Rosenberg et al. study attempt to quantify public sentiment around the NGSS and how that sentiment changes over time. Specifically, they asked:

1.  What is the public sentiment expressed toward the NGSS?
2.  How does sentiment for teachers differ from non-teachers?
3.  How do tweets posted to #NGSSchat differ from those without the hashtag?
4.  How does participation in #NGSSchat relate to the public sentiment individuals express?
5.  How does public sentiment vary over time?

For our text mining case study, we'll use approaches similar to those used by the authors cited above to better understand public discourse surrounding these standards, particularly as they relate to STEM education. We will also try to guage public sentiment around the NGSS, by comparing how much more positive or negative NGSS tweets are relative to CSSS tweets. Specifically, in this case study we'll attempt to answer the following questions:

1.  What are the most frequent words or phrases used in reference to tweets about the CCSS and NGSS?
2.  How does sentiment for NGSS compare to sentiment for CCSS?

### 1c. Load Libraries

#### tidytext 📦

![](img/tidytext.png){width="20%"}

As we'll learn first hand in this module, using tidy data principles can also make many text mining tasks easier, more effective, and consistent with tools already in wide use. The {tidytext} package helps to convert text into data frames of individual words, making it easy to to manipulate, summarize, and visualize text using using familiar functions form the {tidyverse} collection of packages.

Let's go ahead and load the {tidytext} package:

```{r}
library(tidytext)
```

For a more comprehensive introduction to the `tidytext` package, I cannot recommend enough the free and excellent online book, *Text Mining with R: A Tidy Approach* [@silge2017text]. If you're interested in pursuing text analysis using R post Summer Workshop, this will be a go to reference.

#### The vader Package 📦

![](img/vader.jpeg){width="20%"}\

The {vader} package is for the Valence Aware Dictionary for sEntiment Reasoning (VADER), a rule-based model for general sentiment analysis of social media text and specifically attuned to measuring sentiment in microblog-like contexts.

To learn more about the {vader} package and its development, take a look at the article by Hutto and Gilbert (2014), [VADER: A Parsimonious Rule-based Model forSentiment Analysis of Social Media Text](http://comp.social.gatech.edu/papers/icwsm14.vader.hutto.pdf).

Let's go ahead and load the VADER library:

```{r}
library(vader)
```

**Note:** The {vader} package can take quite some time to run on a large datasets like the one we'll be working with, so in our Model section we will examine just a small(ish) subset of tweets.

#### **rtweet 📦 (Optional)**

![](img/rtweet.jpeg){width="20%"}

The {rtweet} package provides users a range of functions designed to extract data from Twitter's REST and streaming APIs and has three main goals:

1.  Formulate and send requests to Twitter's REST and stream APIs.

2.  Retrieve and iterate over returned data.

3.  Wrangling data into tidy structures.

For those that created a Twitter Developer account, load the {rtweet} package that we'll be using to accomplish all three of the goals listed above:

```{r}
library(rtweet)
```

#### Other Packages

Finally, there are a few other packages we'll need to get started. The first two should look familiar while third {wordcloud2} package is handy little package for creating interactive word clouds.

```{r}
library(tidyverse)
library(here)
library(wordcloud2)
```

## 2. WRANGLE

The importance of data wrangling, particularly when working with text, is difficult to overstate. Just as a refresher, wrangling involves the initial steps of going from raw data to a dataset that can be explored and modeled [@krumm2018]. This case study will place a heavy emphasis on preparing text for analysis and in particular we'll learn how to:

a.  **Import Tweets**. First we revisit the familiar `read_csv()` function for reading in our CCSS and NGSS tweets into R. For those of you who created a Twitter Developer Account, we'll also demonstrate the use of the {rtweet} package for downloading data directly from Twitter.
b.  **Restructure Data**. We focus on removing extraneous data using the `select()` and `filter()` functions from {dplyr}, and revisit functions from the [Tidy Your Data Primer](https://rstudio.cloud/learn/primers/4.3) for merging data frames.
c.  **Tidy Text.** Finally, we introduce the {tidytext} package to "tidy" and tokenize our tweets in order to create our data frame for analysis. We also introduce a new join function to remove "stop words" that don't add much value to our analysis.

### 2a. Part 1: Import Tweets from API (Optional)

This section is optional and for those those [setup a Twitter Developer Account and App](https://laser-institute.github.io/laser-sundry/twitter-setup.html#Introduction). The Import Tweets section introduces the following functions from the {rtweet} package for reading Twitter data into R:

-   [`create_token()`](https://cran.r-project.org/web/packages/rtweet/vignettes/auth.html) Sends request to generate authorization tokens for use of the Twitter API.
-   [`search_tweets()`](https://www.rdocumentation.org/packages/rtweet/versions/0.7.0/topics/search_tweets) Pulls up to 18,000 tweets from the last 6-9 days matching provided search terms. 
-   [`search_tweets2()`](https://www.rdocumentation.org/packages/rtweet/versions/0.7.0/topics/search_tweets) Returns data from **multiple** search queries. 
-   [`get_timelines()`](https://www.rdocumentation.org/packages/rtweet/versions/0.7.0/topics/search_tweets) Returns up to 3,200 tweets of one or more specified Twitter users.

#### Authorization methods

Users can create their personal Twitter token in two different ways. THe recommend method is outlined below.

-   Navigate to [developer.twitter.com/en/apps](https://developer.twitter.com/en/apps) and select your Twitter app
-   Click the tab labeled `Keys and tokens` to retrieve your keys.
-   Locate the `Consumer API keys` (aka "API Secret").

![](img/create-app-6.png){width="100%"}

-   Scroll down to `Access token & access token secret` and click `Create`

![](img/create-app-7.png){width="100%"}

-   Copy and paste the four keys (along with the name of your app) into an R script file and pass them along to `create_token()`. Note, these keys are named secret for a reason. I recommend setting up your token in a separate R script than the one that you will eventually share.

```{r, eval=FALSE}
## store api keys (these are fake example values; replace with your own keys)
app_name <- "Educational Text Mining"
api_key <- "tTnrXa68RRRJYB9ECBLVziVrX"
api_secret_key <- "48Lef96R1dT6dtRHnDsgt1GelrdGrWDf23ahUY14VBImvMppY0"
access_token <- "1671928994-DWrq7PRiJBhBHlJ7MoxZpNdi8kvSJS22K3PTJab"
access_token_secret <- "BnQ9fcCLXp95gNWzC6VWoggIW8WYEq8B6o1ytZatNZcNF"

## authenticate via web browser
token <- create_token(
  app = app_name,
  consumer_key = api_key,
  consumer_secret = api_secret_key,
  access_token = access_token,
  access_secret = access_token_secret)
```

#### Authorization in future R sessions

-   The `create_token()` function should automatically save your token as an environment variable for you. So next time you start an R session \[on the same machine\], rtweet should automatically find your token.
-   To make sure it works, restart your R session, run the following code, and again check to make sure the app name and `api_key` match.

```{r}
## check to see if the token is loaded
get_token()
```

That's it!

#### Search Tweets

Since one of our goals for this walkthrough is a very crude replication of the study by Rosenberg et al. (2021), let's begin by introducing the `search_tweets()` function to try reading into R 5,000 tweets containing the NGSS hashtag and store as a new data frame `ngss_all_tweets`.

Type or copy the following code into your R script or console and run:

```{r}
ngss_all_tweets <- search_tweets(q = "#NGSSchat", n=5000)
```

Note that the first argument `q =` that the `search_tweets()` function expects is the search term included in quotation marks and that `n =` specifies the maximum number of tweets

#### [Your Turn]{style="color: green;"} ⤵ {style="font-style: normal; font-variant-caps: normal; letter-spacing: normal; orphans: auto; text-align: start; text-indent: 0px; text-transform: none; white-space: normal; widows: auto; word-spacing: 0px; -webkit-tap-highlight-color: rgba(26, 26, 26, 0.3); -webkit-text-size-adjust: auto; -webkit-text-stroke-width: 0px; text-decoration: none; caret-color: rgb(0, 0, 0); color: rgb(0, 0, 0);"}

View your new `ngss_all_tweets`data frame using the code chunk below and answer the following questions:

```{r, eval=FALSE}
View(ngss_all_tweets)
```

1.  How many tweets did our query using the Twitter API actually return? How many variables?

    -   285 tweets with 90 variables

2.  Why do you think our query pulled in far less than 5,000 tweets requested?

    -   The query only goes back to 20 October. Using the ?search_tweets help function shows that this function limits the query to the past 6-9 days.

3.  Does our query also include retweets? How do you know?

    -   Yes. There is a variable 'is_retweet' in the dataset with values of TRUE/FALSE.

#### Remove Retweets

While not explicitly mentioned in the paper, it's likely the authors removed retweets in their query since a retweet is simply someone else reposting someone else's tweet and would duplicate the exact same content of the original.

Let's use the `include_rts =` argument to remove any retweets by setting it to `FALSE`:

```{r}
ngss_non_retweets <- search_tweets("#NGSSchat", 
                                   n=5000, 
                                   include_rts = FALSE)
```

#### Using the OR Operator

If you recall from the [Data Sources] section above, the authors accessed tweets and user information from the hashtag-based #NGSSchat online community, all tweets that included any of the following phrases, with "/" indicating an additional phrase featuring the respective plural form: "ngss", "next generation science standard/s", "next gen science standard/s".

Let's modify our query using the `OR` operator to also include "ngss" so it will return tweets containing either #NGSSchat or "ngss" and assign to `ngss_or_tweets`:

```{r}
ngss_or_tweets <- search_tweets(q = "#NGSSchat OR ngss", 
                                n=5000, 
                                include_rts = FALSE)
```

#### [Your Turn]{style="color: green;"} ⤵ {style="font-style: normal; font-variant-caps: normal; letter-spacing: normal; orphans: auto; text-align: start; text-indent: 0px; text-transform: none; white-space: normal; widows: auto; word-spacing: 0px; -webkit-tap-highlight-color: rgba(26, 26, 26, 0.3); -webkit-text-size-adjust: auto; -webkit-text-stroke-width: 0px; text-decoration: none; caret-color: rgb(0, 0, 0); color: rgb(0, 0, 0);"}

Try including both search terms but excluding the `OR` operator to answer the following question:

```{r}
ngsschat_ngss_tweets <- search_tweets(q = "NGSSchat ngss", 
                                      n = 5000,
                                      type = "mixed",
                                      include_rts = FALSE)
```

1.  Does excluding the `OR` operator return more tweets, the same number of tweets, or fewer tweets? Why?

    -   Fewer. It's looking for both terms in a single tweet.

2.  What other useful arguments does the `search_tweet()` function contain? Try adding one and see what happens.

    -   I tried type = "popular" and got no returns. "Mixed" came back with my original 26 tweets using both search terms. "Popular" was searching for tweets that hit the magic AI threshold for being popular, which none of these tweets did. "Mixed" brings in a mixture of both "popular" and unpopular? tweets.

            search_tweets(
              q,
              n = 100,
              type = "recent",
              include_rts = TRUE,
              geocode = NULL,
              max_id = NULL,
              parse = TRUE,
              token = NULL,
              retryonratelimit = FALSE,
              verbose = TRUE,
              ...
            )

**Hint:** Use the `?search_tweets` help function to learn more about the `q` argument and other arguments for composing search queries.

#### Use Multiple Queries

Unfortunately, the `OR` operator will only get us so far. In order to include the additional search terms, we will need to use the `c()` function to combine our search terms into a single list.

The `rtweets` package has an additional `search_tweets2()` function for using multiple queries in a search. To do this, either wrap single quotes around a search query using double quotes, e.g., `q = '"next gen science standard"'` or escape each internal double quote with a single backslash, e.g., `q = "\"next gen science standard\""`.

Copy and past the following code to store the results of our query in `ngss_tweets`:

```{r}
ngss_tweets <- search_tweets2(c("#NGSSchat OR ngss", 
                                '"next generation science standard"', 
                                '"next generation science standards"', 
                                '"next gen science standard"', 
                                '"next gen science standards"' ), 
                              n=5000, 
                              include_rts = FALSE)
```

#### Our First Dictionary

Recall that for our research question we wanted to compare public sentiment about both the NGSS and CCSS state standards. Let's go ahead and create our very first "dictionary" for identifying tweets related to either set of standards, and then use that dictionary for our the `q =` query argument to pull tweets related to the state standards.

To do so, we'll need to add some additional search terms to our list:

```{r}
ngss_dictionary <- c("#NGSSchat OR ngss", 
                     '"next generation science standard"', 
                     '"next generation science standards"', 
                     '"next gen science standard"', 
                     '"next gen science standards"') 

ngss_tweets <- search_tweets2(ngss_dictionary, n=5000, include_rts = FALSE)
```

Now let's create a dictionary for the Common Core State Standards and pass that to our `search_tweets()` function to get the most recent tweets:

```{r}
ccss_dictionary <- c("#commoncore", '"common core"') 
ccss_tweets <- 
  ccss_dictionary %>% 
  search_tweets2(n=5000, include_rts = FALSE)
```

Notice that you can use the pipe operator with the `search_tweets()` function just like you would other functions from the tidyverse.

#### [Your Turn]{style="color: green;"} ⤵ {style="font-style: normal; font-variant-caps: normal; letter-spacing: normal; orphans: auto; text-align: start; text-indent: 0px; text-transform: none; white-space: normal; widows: auto; word-spacing: 0px; -webkit-tap-highlight-color: rgba(26, 26, 26, 0.3); -webkit-text-size-adjust: auto; -webkit-text-stroke-width: 0px; text-decoration: none; caret-color: rgb(0, 0, 0); color: rgb(0, 0, 0);"}

1.  Use the `search_tweets` function to create you own custom query for a twitter hashtag or topic(s) of interest.

    ```{r}
    educhat_dictionary <- c("#educhat", "#edutwitter")
    educhat_tweets <-
      educhat_dictionary %>%
      search_tweets2(n = 5000, include_rts = FALSE)
    ```

#### Other Useful Queries

For your independent analysis, you may be interest in exploring posts by specific users rather than topics, key words, or hashtags. Yes, there is a function for that too!

For example, let's create another list containing the usernames of me and some of my colleagues at the Friday Institute using the `c()` function again and use the `get_timelines()` function to get the most recent tweets from each of those users:

```{r}
fi <- c("sbkellogg", "TooSweetGeek", "haspires", "tarheel93", "drcallie_tweets", "AlexDreier") 

fi_tweets <- fi %>% get_timelines(include_rts=FALSE)
```

And let's use the `sample_n()` function from the `dplyr` package to pick 10 random tweets and use `select()` to select and view just the `screenname` and `text` columns that contains the user and the content of their post:

```{r}
sample_n(fi_tweets, 10) %>% select(screen_name, text)
```

We've only scratched the surface of the number of functions available in the `rtweets` package for searching Twitter. Use the following function to learn more about the {rtweet} package:

```{r, eval=F}
vignette("intro", package="rtweet")
```

#### [Your Turn]{style="color: green;"} ⤵ {style="font-style: normal; font-variant-caps: normal; letter-spacing: normal; orphans: auto; text-align: start; text-indent: 0px; text-transform: none; white-space: normal; widows: auto; word-spacing: 0px; -webkit-tap-highlight-color: rgba(26, 26, 26, 0.3); -webkit-text-size-adjust: auto; -webkit-text-stroke-width: 0px; text-decoration: none; caret-color: rgb(0, 0, 0); color: rgb(0, 0, 0);"}

To conclude Section 2a, try one of the following search functions from the `rtweet` vignette:

1.  `get_timelines()` Get the most recent 3,200 tweets from users.

2.  `stream_tweets()` Randomly sample (approximately 1%) from the live stream of all tweets.

3.  `get_friends()` Retrieve a list of all the accounts a user follows.

4.  `get_followers()` Retrieve a list of the accounts following a user.

5.  `get_favorites()` Get the most recently favorited statuses by a user.

6.  `get_trends()` Discover what's currently trending in a city.

7.  `search_users()` Search for 1,000 users with the specific hashtag in their profile bios.

    ```{r}
    get_trends(woeid = "Raleigh")
    ```

### 2a. Part 2: Import Tweets from CSV

First, let's use the by now familiar `read_csv()` and `here()` functions to import our `ccss_tweets.csv` file saved in our data folder:

```{r}
ccss_tweets <- read_csv(here("unit-3", "data", "ccss-tweets.csv"), 
          col_types = cols(author_id = col_character(), 
                           id = col_character(),
                           conversation_id = col_character(), 
                           in_reply_to_user_id = col_character()
                           )
          )
```

Note the addition of the `col_types =` argument for changing some of the column types to character strings because the numbers for those particular columns actually indicate identifiers for authors and tweets:

-   `author_id` = the author of the tweet

-   `id` = the unique id for each tweet

-   `converastion_id` = the unique id for each [conversation thread](https://developer.twitter.com/en/docs/twitter-api/conversation-id)

-   `in_reply_to_user_id` = the author of the tweet being replied to

#### [Your Turn]{style="color: green;"} ⤵ {style="font-style: normal; font-variant-caps: normal; letter-spacing: normal; orphans: auto; text-align: start; text-indent: 0px; text-transform: none; white-space: normal; widows: auto; word-spacing: 0px; -webkit-tap-highlight-color: rgba(26, 26, 26, 0.3); -webkit-text-size-adjust: auto; -webkit-text-stroke-width: 0px; text-decoration: none; caret-color: rgb(0, 0, 0); color: rgb(0, 0, 0);"}

Use the following code chunk to import the NGSS tweets located in the same data folder as our common core tweets. By default, R will treat numerical IDs in our dataset as numeric values but we will need to convert these to characters like demonstrated above for the purpose of analysis:

```{r}
ngss_tweets <- read_csv(here("unit-3", "data", "ngss-tweets.csv"), 
          col_types = cols(author_id = col_character(), 
                           id = col_character(),
                           conversation_id = col_character(), 
                           in_reply_to_user_id = col_character()
                           )
          )
```

Importing data and dealing with data types can be a bit tricky, especially for beginners. Recall from previous case studies that RStudio has an "Import Dataset" feature in the Environment Pane that can help you use the {readr} package and associated functions to greatly facilitate this process. If you get stuck, you can copy the code generated in the lower right hand corner of the Import Dataset window.

![](img/import-data.png)

Now use the following code chunk to inspect the `head()` of each data frame and answer the questions that follow:

```{r}
head(ccss_tweets)
head(ngss_tweets)
```

Wow, so much for a family friendly case study! Based on this very limited sample, which set of standards do you think Twitter users are more negative about?

-   Common Core Science Standards is the more negative query.

Let's take a slightly larger sample of the CCSS tweets:

```{r}
ccss_tweets %>% 
  sample_n(20) %>%
  relocate(text)
           
```

#### [Your Turn]{style="color: green;"} ⤵

Use the code chunk below to take a sample of the NGSS tweets. Try to do it without looking at the code above first:

```{r}
ngss_tweets %>%
  sample_n(20) %>%
  relocate(text)

```

1.  Still of the same opinion?

    -   Yes, these appear much less negative.

2.  What else you notice about our data sets? Record a few observations that you think are relevant to our analysis or might be useful for future analyses.

    -   CCSS tweets begin with hashtags or userIDs while the NGSS tweets look save those features until the end of the tweet.

    -   The negative tweets link to a variety of topics (like politics) not necessarily just common core science.

3.  What questions do you have about these data sets? What are you still curious about?

    -   There are some symbols that exist that confuse me. \n\n in the first CCSS tweet. Is that how a specific character is represented when the .csv is imported? Looking at the original tweet, only a space is there and no character.

### 2b. Restructure Data

#### Subset Tweets

As you may have noticed, we have more data than we need for our analysis and should probably pare it down to just what we'll use.

Let's start with the CCSS tweets first. And since this is a family friendly case study, let's use the `filter()` function introduced in previous labs to filter out rows containing "possibly sensitive" language:

```{r, eval=TRUE}
ccss_tweets_1 <- ccss_tweets %>% 
  filter(possibly_sensitive == "FALSE")
```

Now let's use the `select()` function to select the following columns from our new `ss_tweets_clean` data frame:

1.  `text` containing the tweet which is our primary data source of interest
2.  `author_id` of the user who created the tweet
3.  `created_at` timestamp for examining changes in sentiment over time
4.  `conversation_id` for examining sentiment by conversations
5.  `id` for the unique reference id for each tweet and useful for counts

```{r select-variables, eval=TRUE}
ccss_tweets_2 <- ccss_tweets_1 %>% 
  select(text,
         author_id,
         created_at, 
         conversation_id,
         id)
```

#### [Your Turn]{style="color: green;"} ⤵ {style="font-style: normal; font-variant-caps: normal; letter-spacing: normal; orphans: auto; text-align: start; text-indent: 0px; text-transform: none; white-space: normal; widows: auto; word-spacing: 0px; -webkit-tap-highlight-color: rgba(26, 26, 26, 0.3); -webkit-text-size-adjust: auto; -webkit-text-stroke-width: 0px; text-decoration: none; caret-color: rgb(0, 0, 0); color: rgb(0, 0, 0);"}

**Note:** The `select()` function will also reorder your columns based on the order in which you list them.

Use the code chunk below to reorder the columns to your liking and assign to `ccss_tweets_3`:

```{r}
ccss_tweets_3 <- ccss_tweets_2 %>%
  select(text,
         created_at,
         id,
         author_id,
         conversation_id)
```

#### Add & Relocate Columns

Finally, since we are interested in comparing the sentiment of NGSS tweets with CCSS tweets, it would be helpful if we had a column to quickly identify the set of state standards with which each tweet is associated.

We'll use the `mutate()` function to create a new variable called `standards` to label each tweets as "ccss":

```{r}
ccss_tweets_4 <- mutate(ccss_tweets_2, standards = "ccss")

colnames(ccss_tweets_4)
```

And just because it bothers me, I'm going to use the `relocate()` function to move the `standards` column to the first position so I can quickly see which standards the tweet is from:

```{r}
ccss_tweets_5 <- relocate(ccss_tweets_4, standards)

colnames(ccss_tweets_5)
```

Again, we could also have used the `select()` function to reorder columns like so:

```{r}
ccss_tweets_5 <- ccss_tweets_4 %>% 
  select(standards,
         text,
         author_id,
         created_at, 
         conversation_id,
         id)

colnames(ccss_tweets_5)
```

Before moving on to the CCSS standards, let's use the `%>%` operator and rewrite the code from our wrangling so there is less redundancy and it is easier to read:

```{r}
# Search Tweets
ccss_tweets_clean <- ccss_tweets %>%
  filter(possibly_sensitive == "FALSE") %>%
  select(text, author_id, created_at, conversation_id, id) %>%
  mutate(standards = "ccss") %>%
  relocate(standards)

head(ccss_tweets_clean)
```

#### [Your Turn]{style="color: green;"} ⤵

Recall from section [1b. Define Questions] that we are interested in comparing word usage and public sentiment around both the Common Core and Next Gen Science Standards.

Create an new `ngss_tweets_clean` data frame consisting of the Next Generation Science Standards tweets we imported by using the code above as a guide.

```{r}
ngss_tweets_clean <- ngss_tweets %>%
  filter(possibly_sensitive == "FALSE") %>%
  select(text, author_id, created_at, conversation_id, id) %>%
  mutate(standards = "ngss") %>%
  relocate(standards)

head(ngss_tweets_clean)

```

#### Merge Data Frames

Finally, let's combine our CCSS and NGSS tweets into a single data frame by using the `union()` function from `dplyr` and simply supplying the data frames that you want to combine as arguments:

```{r}
ss_tweets <- union(ccss_tweets_clean,
                   ngss_tweets_clean)

ss_tweets
```

Note that when creating a "union" like this (i.e. stacking one data frame on top of another), **you should have the same number of columns in each data frame and they should be in the exact same order**.

Alternatively, we could have used the `bind_rows()` function from {dplyr} as well:

```{r}
ss_tweets <- bind_rows(ccss_tweets_clean,
                       ngss_tweets_clean)

ss_tweets
```

The distinction between these two functions is that union by default removes any duplicate rows that might have shown up in our queries.

However, since both functions returned the same number of rows, it's clear we do not have any duplicates. If we wanted to verify, {dplyr} also has an intersect function to merge the two data frames, but only where they `intersect()`, or where they have duplicate rows.

```{r}
ss_tweets_duplicate <- intersect(ccss_tweets_clean,
                                 ngss_tweets_clean)
```

#### [Your Turn]{style="color: green;"} ⤵

Finally, let's take a quick look at both the `head()` and the `tail()` of this new `ss_tweets` data frame to make sure it contains both "ngss" and "ccss" standards:

```{r}
head(ss_tweets)
tail(ss_tweets)

```

### 2c. Tidy Text

Text data by it's very nature is ESPECIALLY untidy and is sometimes referred to as "unstructured" data. In this section we learn some very useful functions from the {tidytext} package to convert text to and from tidy formats. Having our text in a tidy format will allow us to switch seamlessly between tidy tools and existing text mining packages, while also making it easier to visualize text summaries in other data analysis tools like Tableau.

#### Tokenize Text {data-link="2b. Tidy Text"}

In Chapter 1 of Text Mining with R, @silge2017text define the tidy text format as a table with one-token-per-row, and explain that:

> A **token** is a meaningful unit of text, such as a word, two-word phrase (bigram), or sentence that we are interested in using for analysis. And tokenization is the process of splitting text into tokens.

This one-token-per-row structure is in contrast to the ways text is often stored for text analysis, perhaps as strings in a corpus object or in a document-term matrix. For tidy text mining, the token that is stored in each row is most often a single word, but can also be an n-gram, sentence, or paragraph.

For this part of our workflow, our goal is to transform our `ss_tweets` data from this:

```{r}
head(relocate(ss_tweets, text))
```

Into a "tidy text" one-token-per-row format that looks like this:

```{r}
tidy_tweets <- ss_tweets %>% 
  unnest_tokens(output = word, 
                input = text) %>%
  relocate(word)

head(tidy_tweets)
```

If you take ECI 588: Text Mining in Education, you'll learn about other data structures for text analysis like the document-term matrix and corpus objects. For now, however, working with the familiar tidy data frame allows us to take advantage of popular packages that use the shared tidyverse syntax and principles for wrangling, exploring, and modeling data.

As demonstrated above, the `tidytext` package provides the incredibly powerful `unnest_tokens()` function to tokenize text (including tweets!) and convert them to a one-token-per-row format.

Let's tokenize our tweets by using this function to split each tweet into a single row to make it easier to analyze and take a look:

```{r unnest-tokens}
ss_tokens <- unnest_tokens(ss_tweets, 
                           output = word, 
                           input = text)

head(relocate(ss_tokens, word))
```

There is A LOT to unpack with this function:

-   First notice that `unnest_tokens()` expects a data frame as the first argument, followed by two column names.
-   The next argument is an output column name that doesn't currently exist but will be created as the text is "unnested" into it, `word` in this case).
-   This is followed by the input column that the text comes from, which we uncreatively named `text`.
-   By default, a token is an individual word or unigram.
-   Other columns, such as `author_id` and `created_at`, are retained.
-   All punctuation has been removed.
-   Tokens have been changed to lowercase, which makes them easier to compare or combine with other datasets (use the `to_lower = FALSE` argument to turn off if desired).

**Note:** Since {tidytext} follows tidy data principles, we also could have used the `%>%` operator to pass our data frame to the `unnest_tokens()` function like so:

```{r}
ss_tokens <- ss_tweets %>%
  unnest_tokens(output = word, 
                input = text)

ss_tokens
```

#### [Your Turn]{style="color: green;"} ⤵ {style="font-style: normal; font-variant-caps: normal; letter-spacing: normal; orphans: auto; text-align: start; text-indent: 0px; text-transform: none; white-space: normal; widows: auto; word-spacing: 0px; -webkit-tap-highlight-color: rgba(26, 26, 26, 0.3); -webkit-text-size-adjust: auto; -webkit-text-stroke-width: 0px; text-decoration: none; caret-color: rgb(0, 0, 0); color: rgb(0, 0, 0);"}

The `unnest_tokens()` function also has a specialized `“tweets”` tokenizer in the `tokens =` argument that is very useful for dealing with Twitter text. It retains hashtags and mentions of usernames with the \@ symbol as illustrated by our \@catturd2 friend who featured prominently in our the first CCSS tweet.

Rewrite the code above (**you can check answer below**) to include the token argument set to "tweets", assign to `ss_tokens_1`, and answer the questions that follow:

```{r}
ss_tokens <- unnest_tokens(ss_tweets, 
                           token = "tweets",
                           output = word, 
                           input = text)

head(relocate(ss_tokens, word))

```

1.  How many observations were our original `ss_tweets` data frame?

    -   35,233

2.  How many observations are there now? Why the difference?

    -   874,214. Each word in every tweet is now it's own observation (row).

Before we move any further let's take a quick look at the most common word in our two datasets. To do so, we'll introduce the easy to use `count()` function from the {dplyr} package.

Like most functions we've introduced, the first argument `count()` expects is a data frame which we provided with the `%>%` operator, followed but the column, in our case `word`, whose values we want to count:

```{r}
ss_tokens %>%
  count(word, sort = TRUE)

```

Well, many of these tweets are clearly about the CCSS and math at least, but beyond that it's a bit hard to tell because there are so many "stop words" like "the", "to", "and", "in" that don't carry much meaning by themselves.

#### Remove Stop Words

Often in text analysis, we will want to remove these stop words if they are not useful for an analysis. The `stop_words` dataset in the {tidytext} package contains stop words from three lexicons. We can use them all together, as we have here, or `filter()` to only use one set of stop words if that is more appropriate for a certain analysis.

Let's take a closer the lexicons and stop words included in each:

```{r, eval=FALSE}
View(stop_words)
```

#### The `anti_join` Function

In order to remove these stop words, we will use a function called `anti_join()` that looks for matching values in a specific column from two datasets and returns rows from the original dataset that have no matches like so:

![](img/anti-join.png)

For a good overview of the different `dplyr` joins see here: <https://medium.com/the-codehub/beginners-guide-to-using-joins-in-r-682fc9b1f119>.

Now let's remove stop words that don't help us learn much about what people are saying about the state standards.

```{r stop-unigrams}
ss_tokens_2 <- anti_join(ss_tokens,
                         stop_words,
                         by = "word")

head(ss_tokens_2)
```

Notice that we've specified the `by =` argument to look for matching words in the `word` column for both data sets and remove any rows from the `tweet_tokens` dataset that match the `stop_words` dataset. Remember when we first tokenized our dataset I conveniently chose `output = word` as the column name because it matches the column name `word` in the `stop_words` dataset contained in the `tidytext` package. This makes our call to `anti_join()`simpler because `anti_join()` knows to look for the column named `word` in each dataset. However this wasn't really necessary since `word` is the only matching column name in both datasets and it would have matched those columns by default.

#### [Your Turn]{style="color: green;"} ⤵ {style="font-style: normal; font-variant-caps: normal; letter-spacing: normal; orphans: auto; text-align: start; text-indent: 0px; text-transform: none; white-space: normal; widows: auto; word-spacing: 0px; -webkit-tap-highlight-color: rgba(26, 26, 26, 0.3); -webkit-text-size-adjust: auto; -webkit-text-stroke-width: 0px; text-decoration: none; caret-color: rgb(0, 0, 0); color: rgb(0, 0, 0);"}

Use the code chunk below to take a quick count of the most common tokens in our `ss_tweets_2` data frame to see if the results are a little more meaningful, then answer the questions that follow.

```{r}
ss_tokens_2 %>%
  count(word, sort = TRUE)
```

#### [Your Turn]{style="color: green;"} ⤵ {style="font-style: normal; font-variant-caps: normal; letter-spacing: normal; orphans: auto; text-align: start; text-indent: 0px; text-transform: none; white-space: normal; widows: auto; word-spacing: 0px; -webkit-tap-highlight-color: rgba(26, 26, 26, 0.3); -webkit-text-size-adjust: auto; -webkit-text-stroke-width: 0px; text-decoration: none; caret-color: rgb(0, 0, 0); color: rgb(0, 0, 0);"}

1.  How many unique tokens are in our data tidied text?

    -   73,595

2.  How many times does the word "math" occur in our set of tweets?

    -   11,788

#### Custom Stop Words

Notice that the nonsense word "amp" is among our high frequency words as well as some. We can create our own custom stop word list to to weed out any additional words that don't carry much meaning but skew our data by being so prominent.

Let's create a custom stop word list by using the simple `c()` function to combine our words. We can the add a filter to keep rows where words in our `word` column do NOT `!` match words `%in%` `my_stopwords` list:

```{r}
my_stopwords <- c("amp", "=", "+")

ss_tokens_3 <-
  ss_tokens_2 %>%
  filter(!word %in% my_stopwords)
```

Let's take a look at our top words again and see if that did the trick:

```{r}
ss_tokens_3 %>%
  count(word, sort = TRUE)
```

Much better! Note that we could extend this stop word list indefinitely. Feel free to use the code chunk below to try adding more words to our stop list.

Before we move any further, let's save our tidied tweets as a new data frame for Section 3 and also save it as a .csv file in our data folder:

```{r}
ss_tidy_tweets <- ss_tokens_3

write_csv(ss_tokens_3, here("unit-3", "data", "ss_tidy_tweets.csv"))
```

## 3. EXPLORE

Calculating summary statistics, data visualization, and feature engineering (the process of creating new variables from a dataset) are a key part of exploratory data analysis. For our first lab, we're going to keep things super simple and focus on:

a.  **Top Tokens.** Since once of our goals is to compare tweets about the NGSS and CSSS standards, we'll take a look at the top 50 words that appear in each.

b.  **Word Clouds.** To help illustrate the relative frequency each of these top 50 words occurs, we'll introduce the {wordclouds2} package for creating interactive word clouds that can be knitted with your HTML doc.

### 3a. Top Tokens

First, let's take advantage of the the `%>%` operator combine some of the functions we've used above with the `top_n()` function from the {dplyr} package. By default, this function is looking for a data frame as the first argument, and then the number of rows to return.

Let's take a look at the top tokens among the CCSS tweets by filtering our `standards` by CCSS, counting the number of times each `word` occurs, and taking the look at the 50 most common words:

```{r}
ccss_top_tokens <- ss_tidy_tweets %>%
  filter(standards == "ccss") %>%
  count(word, sort = TRUE) %>%
  top_n(50)

ccss_top_tokens
```

Not surprisingly, our search terms appear in the top 50 but the word "math" also features prominently among CCSS tweets!

### Word Clouds

Word clouds are much maligned and sometimes referred to as the "pie charts of text analysis", but they can be useful for communicating simple summaries of qualitative data for education practitioners and are intuitive for them to interpret. Also, for better or worse, these are now included as a default visualization for open-ended survey items in online Qualtrics reports and you can even add your own stop words.

The {wordclouds2} package is pretty dead simple tool for generating HTML based interactive word clouds. By default, when you pass a data frame to the `wordcloud2()` function, it will look for a word column and a column with frequencies or counts, i.e., our column `n` that we created with the `count()` function.

Let's run the `wordcloud2()` function on our `ccss_top_tokens` data frame.

```{r}

wordcloud2(ccss_top_tokens)
```

As you can see, "math" is a pretty common topic when discussing the common core on twitter but words like "core" and "common" -- which you can see better if you click the "show in a new window" button or run the code in you console -- are not very helpful since those were in our search terms when pulling data from Twitter.

In fact, search terms like these we might want to exclude from a final data product we share with with education partners or in a publication and instead include these these in a title or caption.

```{r}

ccss_top_tokens %>%
  filter(word != "common" & word != "core") %>%
  wordcloud2()
```

#### [Your Turn]{style="color: green;"} ⤵ {style="font-style: normal; font-variant-caps: normal; letter-spacing: normal; orphans: auto; text-align: start; text-indent: 0px; text-transform: none; white-space: normal; widows: auto; word-spacing: 0px; -webkit-tap-highlight-color: rgba(26, 26, 26, 0.3); -webkit-text-size-adjust: auto; -webkit-text-stroke-width: 0px; text-decoration: none; caret-color: rgb(0, 0, 0); color: rgb(0, 0, 0);"}

In the code chunk below, filter, count and select the top 50 tokens to create a word cloud for the NGSS tweets. A gold star if you can can do it without using the assignment operator or looking at the code above!

```{r}
ss_tidy_tweets %>%
  filter(standards == "ngss",
         word != "ngss" & word != "#ngss" & word != "@ngsstweeps") %>%
  count(word, sort = TRUE) %>%
  top_n(50) %>%
  wordcloud2(shape = 'pentagon')
```

Also, take a look at the help file for `wordclouds2` to see if there might be other ways you could improve the aesthetics of this visualization.

## 4. MODEL

Now that we have our tweets nice and tidy, we're almost ready to begin exploring public sentiment (at least for the past week due to Twitter API rate limits) around the CCSS and NGSS standards. For this part of our workflow we introduce two new functions from the [`tidytext`](https://github.com/juliasilge/tidytext) and `dplyr` packages respectively:

#### How do you "measure" sentiment?

Sentiment analysis tries to evaluate words for their emotional association. In [Text Mining with R: A Tidy Approach](https://www.tidytextmining.com/sentiment.html), Silge aand Robinson point out that,

> One way to analyze the sentiment of a text is to consider the text as a combination of its individual words and the sentiment content of the whole text as the sum of the sentiment content of the individual words.

This isn't the only way to approach sentiment analysis, but it is an easier entry point into sentiment analysis and you'll find that is it often-used in publications that utilize sentiment analysis.

The {tidytext} package provides access to several sentiment **lexicons,** [sometimes referred to as dictionaries](https://cbail.github.io/textasdata/dictionary-methods/rmarkdown/Dictionary-Based_Text_Analysis.html), based on unigrams, i.e., single words. These lexicons contain many English words and the words are assigned scores for positive/negative sentiment, and also possibly emotions like joy, anger, sadness, and so forth.

The three general-purpose lexicons we'll focus on are:

-   `AFINN` assigns words with a score that runs between -5 and 5, with negative scores indicating negative sentiment and positive scores indicating positive sentiment.

-   `bing` categorizes words in a binary fashion into positive and negative categories.

-   `nrc` categorizes words in a binary fashion ("yes"/"no") into categories of positive, negative, anger, anticipation, disgust, fear, joy, sadness, surprise, and trust.

Note that **if this is your first time using the AFINN and NRC lexicons, you may prompted to download both** Respond yes to the prompt by entering "1" and the NRC and AFINN lexicons will download. You'll only have to do this the first time you use the NRC lexicon.

Let's take a quick look at each of these lexicons using the `get_sentiments()` function and assign them to their respective names for later use:

```{r}
afinn <- get_sentiments("afinn")

afinn
```

```{r}
bing <- get_sentiments("bing")

bing
```

```{r}
nrc <- get_sentiments("nrc")

nrc
```

And just out of curiosity, let's take a look at the `loughran` lexicon as well:

```{r}
loughran <- get_sentiments("loughran")

loughran
```

#### [Your Turn]{style="color: green;"} ⤵ {style="font-style: normal; font-variant-caps: normal; letter-spacing: normal; orphans: auto; text-align: start; text-indent: 0px; text-transform: none; white-space: normal; widows: auto; word-spacing: 0px; -webkit-tap-highlight-color: rgba(26, 26, 26, 0.3); -webkit-text-size-adjust: auto; -webkit-text-stroke-width: 0px; text-decoration: none; caret-color: rgb(0, 0, 0); color: rgb(0, 0, 0);"}

1.  How were these sentiment lexicons put together and validated? Hint: take a look at [Chapter 2 from Text Mining with R](https://www.tidytextmining.com/sentiment.html).

    -   They were constructed via either crowdsourcing (using, for example, Amazon Mechanical Turk) or by the labor of one of the authors, and were validated using some combination of crowdsourcing again, restaurant or movie reviews, or Twitter data.

#### Come to the Dark Side

![](img/dark-side.jpeg){width="80%"}

As noted in the PERPARE section, the {vader} package is for the Valence Aware Dictionary for sEntiment Reasoning (VADER), a rule-based model for general sentiment analysis of social media text and specifically attuned to measuring sentiment in microblog-like contexts such as Twitter.

The VADER assigns a number of different sentiment measures based on the context of the entire social-media post or in our case a tweet. Ultimately, however, these measures are based on a sentiment lexicon similar to those you just saw above. One benefit of using VADER rather than the approaches described by Silge and Robinson is that we use it with our tweets in their original format and skip the text preprocessing steps demonstrated above.

One drawback to VADER is that it can take a little while to run since it's computationally intensive. Instead of analyzing tens of thousands of tweets, let's read in our original `ccss-tweets.csv` and take instead just a sample of 500 "untidu" CCSS tweets using the `sample_n()` function:

```{r}
ccss_sample <- read_csv(here("unit-3", "data", "ccss-tweets.csv")) %>%
  sample_n(500)

ccss_sample
```

Note above that we passed our `read_csv()` output directly to our `sample()` function rather than saving a new data frame object, passing that to `sample_n()`, and saving as another data frame object. The power of the `%>%` pipe!

On to the Dark Side. The {vader} package basically has just one function, `vader_df()` that does one thing and expects just one column from one frame. He's very single minded! Let's give VADER our `ccss_sample` data frame and include the `$` operator to include only the `text` column containing our tweets.

```{r}
vader_ccss <- vader_df(ccss_sample$text)

vader_ccss
```

Take a look at `vader_summary` data frame using the `View()` function in the console and sort by most positive and negative tweets.

Does it generally seem accurately identify positive and negative tweets? Could you find any that you think were mislabeled?

-   Generally they are classified appropriately. I did find 1 instance (#290) that was labeled positive but it's sarcasm and is really negative.

Hutto, C. & Gilbert, E. (2014) provide an excellent summary of the VADER package on their [GitHub repository](scores) and I've copied and explanation of the scores below:

-   The `compound` score is computed by summing the valence scores of each word in the lexicon, adjusted according to the rules, and then normalized to be between -1 (most extreme negative) and +1 (most extreme positive). This is the most useful metric if you want a single unidimensional measure of sentiment for a given sentence. Calling it a 'normalized, weighted composite score' is accurate.

**NOTE:** The `compound` score is the one most commonly used for sentiment analysis by most researchers, including the authors.

Let's take a look at the average compound score for our CCSS sample of tweets:

```{r}
mean(vader_ccss$compound)
```

Overall, does your CCSS tweets sample lean slightly negative or positive? Is this what you expected?

What if we wanted to compare these results more easily to our other sentiment lexicons just to check if result are fairly consistent?

The author's note that it is also useful for researchers who would like to set standardized thresholds for classifying sentences as either positive, neutral, or negative. Typical threshold values are:

-   **positive sentiment**: `compound` score >= 0.05

-   **neutral sentiment**: (`compound` score > -0.05) and (`compound` score \< 0.05)

-   **negative sentiment**: `compound` score \<= -0.05

Let's give that a try and see how things shake out:

```{r}
vader_ccss_summary <- vader_ccss %>% 
  mutate(sentiment = ifelse(compound >= 0.05, "positive",
                            ifelse(compound <= -0.05, "negative", "neutral"))) %>%
  count(sentiment, sort = TRUE) %>% 
  spread(sentiment, n) %>% 
  relocate(positive) %>%
  mutate(ratio = negative/positive)

vader_ccss_summary
```

Not quite as bleak as we might have expected according to VADER! But then again, VADER brings an entirely different perspective coming from the dark side

#### [Your Turn]{style="color: green;"} ⤵ {style="font-style: normal; font-variant-caps: normal; letter-spacing: normal; orphans: auto; text-align: start; text-indent: 0px; text-transform: none; white-space: normal; widows: auto; word-spacing: 0px; -webkit-tap-highlight-color: rgba(26, 26, 26, 0.3); -webkit-text-size-adjust: auto; -webkit-text-stroke-width: 0px; text-decoration: none; caret-color: rgb(0, 0, 0); color: rgb(0, 0, 0);"}

In a separate R script file, try using VADER to perform a sentiment analysis of the NGSS tweets and see how they compare. Post your working code in the chunk below and answer the questions that follow:

```{r}
# Create 500-instance sample of NGSS dataset
ngss_sample <- read_csv(here("unit-3", "data", "ngss-tweets.csv")) %>%
  sample_n(500)

# Vader the NGSS sample
vader_ngss <- vader_df(ngss_sample$text)

# Compute average compound sentiment of NGSS tweets
mean(vader_ngss$compound)

# Summarize Vader Sentiment Analysis
vader_ngss_summary <- vader_ngss %>% 
  mutate(sentiment = ifelse(compound >= 0.05, "positive",
                            ifelse(compound <= -0.05, "negative", "neutral"))) %>%
  count(sentiment, sort = TRUE) %>% 
  spread(sentiment, n) %>% 
  relocate(positive) %>%
  mutate(ratio = negative/positive)

vader_ngss_summary

```

1.  How do our results compare to the CSSS sample of tweets?

    -   Much more uniformly positive. Ratio was 0.13.

2.  How do these result compare to those found by Rosenberg et al. (2021)?

    -   My results were slightly less positive has his findings resulted in a ratio of 0.11, but still relatively close. I expect the difference was due to my sample size.

## 5. COMMUNICATE

In this case study, we focused on the literature guiding our analysis; wrangling our data into a one-token-per-row tidy text format; using simple word counts and word clouds to explore our data; and comparing sentiment in tweets about the NGSS and CCSS curriculum standards.

In response to the following research questions driving this analysis, write a 2-3 sentences summarizing our findings:

1.  What are the most frequent words or phrases used in reference to tweets about the CCSS and NGSS?

    -   Math, education, and standards were the most common terms in the CCSS tweets.

    -   Science, students, and learning were the big 3 in the NGSS tweets.

    -   The most common terms were all single words as we used unigrams as opposed to any groupings for phrases.

2.  How does sentiment for NGSS compare to sentiment for CCSS?

    -   NGSS tweet sentiment was overwhelmingly positive (by a margin of almost 10:1), while the CCSS tweets were more neutrally balanced.

Finally, add 1-2 sentences in response to the following prompts:

1.  One important thing I took away from this case study about text mining :

    -   How you tidy the data has a huge impact on the types of insights you can draw. Having a good plan on how to tidy based on your research questions is key to your success.

2.  One thing about text mining I want to learn more about:

    -   Are there text mining dictionaries based on mis/disinformation? Or this that too context-dependent? In other words, can we use similar methods to determine if a group of words is true or false?

Congratulations - you've completed your first text mining case study! To complete your work, you can click the drop down arrow at the top of the file, then select "Knit top HTML". This will create a report in your Files pane that serves as a record of your code and its output you can open or share.

## Extra Credit (Optional)

### Sentiment Analysis

In a separate R script file, and using your own text data or data that you you pulled from Twitter above, try tidying your data into a tidy text format, examining the top words in your dataset, and conducting sentiment analysis with VADER. Post your final code here:

```{r}
# Import news articles from real and fake source files
news_real <- read_csv(here("unit-3", "data", "news_real.csv"),
                 show_col_types = FALSE)

news_fake <- read_csv(here("unit-3", "data", "news_fake.csv"),
                      show_col_types = FALSE)

# Relocate class column to first position
news_real_1 <- relocate(news_real, class)

news_fake_1 <- relocate(news_fake, class)

# Reduce dataframe size to a randome 450 instances for simpler analysis
news_real_2 <- news_real_1[sample(1:nrow(news_real_1), 450), ]

news_fake_2 <- news_fake_1[sample(1:nrow(news_fake_1), 450), ]

# Merge real and fake dataframes
news <- union(news_real_2,
              news_fake_2)

# Tokenize news article text
news_tokens <- news %>% 
  unnest_tokens(output = word, 
                input = text) %>%
  relocate(word)

# Remove stop words
news_tokens_1 <- anti_join(news_tokens,
                         stop_words,
                         by = "word")

# Save tidy news data as .csv
news_tidy <- news_tokens_1

write_csv(news_tokens_1, here("unit-3", "data", "news_tidy.csv"))

# Measure sentiment of fake news stories
vader_fake <- vader_df(news_fake_2$text)

# Summarize fake news sentiment analysis
vader_fake_summary <- vader_fake %>% 
  mutate(sentiment = ifelse(compound >= 0.05, "positive",
                            ifelse(compound <= -0.05, "negative", "neutral"))) %>%
  count(sentiment, sort = TRUE) %>% 
  spread(sentiment, n) %>% 
  relocate(positive) %>%
  mutate(ratio = negative/positive)

# Measure sentiment of real news stories
vader_real <- vader_df(news_real_2$text)

# Summarize real news sentiment analysis
vader_real_summary <- vader_real %>% 
  mutate(sentiment = ifelse(compound >= 0.05, "positive",
                            ifelse(compound <= -0.05, "negative", "neutral"))) %>%
  count(sentiment, sort = TRUE) %>% 
  spread(sentiment, n) %>% 
  relocate(positive) %>%
  mutate(ratio = negative/positive)
```

```{r}
# Review top 50 tokens for fake news
fake_top_tokens <- news_tidy %>%
  filter(class == "Fake") %>%
  count(word, sort = TRUE) %>%
  top_n(50)

# Create word cloud for fake news
wordcloud2(fake_top_tokens)
```

```{r}
# Review top 50 tokens for real news
real_top_tokens <- news_tidy %>%
  filter(class == "Real") %>%
  count(word, sort = TRUE) %>%
  top_n(50)

# Create word cloud for fake news, filter for unnecessary words
real_top_tokens %>%
  filter(word != "reuters" & word != "u.s") %>%
  wordcloud2()
```

```{r}
vader_fake_summary
vader_real_summary
```

In general, news was fairly negative around the 2016 elections. That said, **fake** news trended slightly *less negative* than did the **real** news articles.

### Exploring Bigrams

If you'd like to use the data we've been working with for extra credit, let's take a quick look at text analysis using **bigrams**, or tokens consisting of two words.

So far in this lab, we specified tokens as individual words, but many interesting text analyses are based on the relationships between words, which words tend to follow others immediately, or words that tend to co-occur within the same documents.

We can also use the `unnest_tokens()` function to tokenize our tweets into consecutive sequences of words, called **n-grams**. By seeing how often word X is followed by word Y, we could then build a model of the relationships between them.

To specify our tokens as bigrams, We do add `token = "ngrams"` to the `unnest_tokens()` function and setting `n` to the number of words in each n-gram. Let's set `n` to 2, so we can examine pairs of two consecutive words, often called "bigrams":

```{r ccss-bigrams}
ngss_bigrams <- ngss_tweets %>% 
  unnest_tokens(bigram, 
                text, 
                token = "ngrams", 
                n = 2)
```

Before we move any further let's take a quick look at the most common bigrams in our NGSS tweets:

```{r}
ngss_bigrams %>% 
  count(bigram, sort = TRUE)
```

As we saw above, a lot of the most common bigrams are pairs of common (uninteresting) words as well. Dealing with these is a little less straightforward and we'll need to use the `separate()` function from the `tidyr` package, which splits a column into multiple based on a delimiter. This lets us separate it into two columns, "word1" and "word2", at which point we can remove cases where either is a stop-word.

```{r stop-bigrams}
library(tidyr)
bigrams_separated <- ngss_bigrams %>%
  separate(bigram, c("word1", "word2"), sep = " ")

bigrams_filtered <- bigrams_separated %>%
  filter(!word1 %in% stop_words$word) %>%
  filter(!word2 %in% stop_words$word)

tidy_bigrams <- bigrams_filtered %>%
  unite(bigram, word1, word2, sep = " ")
```

Let's take a look at our bigram counts now:

```{r}
tidy_bigrams %>% 
  count(bigram, sort = TRUE)
```

Better, but there are still many tokens not especially useful for analysis.

Let's make a custom custom stop word dictionary for bigrams just like we did for our unigrams. A list is started for you below, but you likely want to expand our list off stop words:

```{r}
my_words <- c("https", "t.co", "ngss_tweeps")
```

Now let's separate, filter, and unite again:

```{r}
tidy_bigrams <- bigrams_separated %>%
  filter(!word1 %in% stop_words$word) %>%
  filter(!word2 %in% stop_words$word) %>%
  filter(!word1 %in% my_words) %>%
  filter(!word2 %in% my_words) %>%
  unite(bigram, word1, word2, sep = " ")
```

Note that since `my_words` is just a vector of words and not a data frame like `stop_words`, we do not need to select the `word` column using the `$` operator.

Let's take another quick count of our bigrams:

```{r}
tidy_bigrams %>% 
  count(bigram, sort = TRUE)
```

#### [Your Turn]{style="color: green;"} ⤵ {style="font-style: normal; font-variant-caps: normal; letter-spacing: normal; orphans: auto; text-align: start; text-indent: 0px; text-transform: none; white-space: normal; widows: auto; word-spacing: 0px; -webkit-tap-highlight-color: rgba(26, 26, 26, 0.3); -webkit-text-size-adjust: auto; -webkit-text-stroke-width: 0px; text-decoration: none; caret-color: rgb(0, 0, 0); color: rgb(0, 0, 0);"}

Use the code chunk below to tidy and count our bigrams for the CCSS tweets:

```{r}
ccss_bigrams <- ccss_tweets %>% 
  unnest_tokens(bigram, 
                text, 
                token = "ngrams", 
                n = 2)

ccss_bigrams %>% 
  count(bigram, sort = TRUE)

ccss_bigrams_separated <- ccss_bigrams %>%
  separate(bigram, c("word1", "word2"), sep = " ")

ccss_bigrams_filtered <- ccss_bigrams_separated %>%
  filter(!word1 %in% stop_words$word) %>%
  filter(!word2 %in% stop_words$word)

ccss_tidy_bigrams <- ccss_bigrams_filtered %>%
  unite(bigram, word1, word2, sep = " ")
```

```{r}
my_words <- c("https", "t.co", "common", "core")
```

```{r}
ccss_tidy_bigrams <- ccss_bigrams_separated %>%
  filter(!word1 %in% stop_words$word) %>%
  filter(!word2 %in% stop_words$word) %>%
  filter(!word1 %in% my_words) %>%
  filter(!word2 %in% my_words) %>%
  unite(bigram, word1, word2, sep = " ")

ccss_tidy_bigrams %>% 
  count(bigram, sort = TRUE)
```

What additional insight, if any, did looking at bigrams bring to out analysis?

-   You can identify specific personalities (Bill Gates, Argo Brothers) that could be involved with Common Core issues. You also see other issues arise due to 2-word context such as "public schools, critical thinking, and commoncore technology.

### References

Note: Citations embedded in R Markdown will only show upon knitting.
