Public Sentiment and the State Standards
Unit 3 Case Study
Dr. Shaun Kellogg
October 31, 2021
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 <- "Text Mining in Education"
api_key <- "afYS4vbIlPAj096E60c4W1fiK"
api_secret_key <- "bI91kqnqFoNCrZFbsjAWHD4gJ91LQAhdCJXCj3yscfuULtNkuu"
access_token <- "9551451262-wK2EmA942kxZYIwa5LMKZoQA4Xc2uyIiEwu2YXL"
access_token_secret <- "9vpiSGKg1fIPQtxc5d5ESiFlZQpfbknEN1f1m2xe5byw7"
## 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:
How many tweets did our query using the Twitter API actually return? How many variables?
Why do you think our query pulled in far less than 5,000 tweets requested?
Does our query also include retweets? How do you know?
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,
##ainclude_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:
Does excluding the
ORoperator return more tweets, the same number of tweets, or fewer tweets? Why?What other useful arguments does the
search_tweet()function contain? Try adding one and see what happens.
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.
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)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.
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?
- I think Twitter users are more negative about CCSS than NGSS.
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 "One time in … 2021-04-22 17:18:16 7512531043… 13852… 13852818142986… Twitte…
## 2 "The Internet… 2021-02-26 11:28:10 7590511752… 13652… 13652623754363… Twitte…
## 3 "“Sedition” s… 2021-01-14 22:57:16 124273082 13498… 13498531143005… Twitte…
## 4 "Must be that… 2021-03-09 03:23:08 1162779383… 13691… 13691265797130… Twitte…
## 5 "@thepennyhoa… 2021-04-10 12:11:46 1350816988… 13808… 13788312940549… Twitte…
## 6 "And Common C… 2021-01-29 09:42:37 1252011926… 13550… 13550889518334… Twitte…
## 7 "@overtaxed23… 2021-01-21 18:16:48 547269089 13523… 13523075704516… Twitte…
## 8 "@KMJeezy I s… 2021-01-15 17:43:04 796348812 13501… 13501355336332… Twitte…
## 9 "@fimail424 @… 2021-04-02 04:41:55 1013326455… 13778… 13776242994277… Twitte…
## 10 "@JonFlan It'… 2021-05-03 09:14:44 1369652744… 13891… 13887405544528… Twitte…
## 11 "This is givi… 2021-05-05 10:39:16 1219278822… 13898… 13898924470403… Twitte…
## 12 "Mobi Free Co… 2021-02-05 06:29:28 1356131629… 13575… 13575770605463… Twitte…
## 13 "@glennfoot77… 2021-01-19 02:54:23 1286038347… 13513… 13512687634679… Twitte…
## 14 "@girlmaui @r… 2021-01-22 03:56:21 7732812426… 13524… 13524369288435… Twitte…
## 15 "@OlyWebDiva … 2021-04-01 18:42:35 263547117 13776… 13776845988057… Twitte…
## 16 "@IngrahamAng… 2021-05-24 15:03:43 1964123797 13968… 13960220165414… Twitte…
## 17 "@paulsperry_… 2021-01-10 04:00:57 387518281 13481… 13479614287838… Twitte…
## 18 "@Ruby55 @Sha… 2021-05-27 02:57:39 175150086 13977… 13769186210253… Twitte…
## 19 "FOX said the… 2021-01-06 18:00:45 1036031508… 13468… 13468793928579… Twitte…
## 20 "@MrReed_FR C… 2021-01-05 14:17:53 1010535328… 13464… 13464597604980… Twitte…
## # … 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 "Looking for … 2021-05-24 12:06:55 206019741 13967… 13967998710431… Twitte…
## 2 "The Next Gen… 2021-05-11 17:05:00 2496339253 13921… 13921638450245… Twitte…
## 3 "@TedWillard2… 2021-01-22 02:34:19 3164721571 13524… 13524438740447… TweetD…
## 4 "@sbottasulli… 2021-05-21 23:58:05 1010324664… 13958… 13958377776698… Twitte…
## 5 "@rletissier … 2021-01-07 18:57:39 1342104169 13472… 13472410492410… Twitte…
## 6 "A6: Someone … 2021-03-05 03:01:17 40062074 13676… 13676715290482… TweetD…
## 7 "I feel that … 2021-01-10 19:00:55 2347485962 13483… 13483440865755… Twitte…
## 8 "Have you see… 2021-02-23 21:53:18 2482150815 13643… 13643325345831… Twitte…
## 9 "#sketchandte… 2021-03-18 02:30:48 14080892 13723… 13723749013618… Twitte…
## 10 "Recently sta… 2021-03-14 22:50:03 1225557977… 13712… 13712321849258… Twitte…
## 11 "Student mode… 2021-03-12 13:11:41 1266418724… 13703… 13703618582014… Twitte…
## 12 "The lifecycl… 2021-05-17 13:14:48 7946080335… 13942… 13942802393967… Twitte…
## 13 "@NGSS_tweeps… 2021-05-19 13:06:45 16394461 13950… 13949814160191… Twitte…
## 14 "My new licen… 2021-03-06 20:59:58 182021246 13683… 13683053774361… Twitte…
## 15 "Just like th… 2021-01-14 21:00:01 4918507542 13498… 13498236091796… TweetD…
## 16 "Anyone looki… 2021-01-27 14:36:06 2182674452 13544… 13544380368603… Twitte…
## 17 "Ack!! I miss… 2021-01-22 03:37:19 862286744 13524… 13524603084559… Twitte…
## 18 "A2: Students… 2021-05-21 01:19:37 3193696255 13955… 13955498103595… TweetD…
## 19 "Just had a Z… 2021-02-16 20:50:00 184649645 13617… 13617798870448… Twitte…
## 20 "@PegGrafwall… 2021-03-17 02:42:08 1040354607… 13720… 13719723426252… 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 seem to include things not as relative to common core (politics, etc.). It also seems like there are more hashtags being used in the NGSS data set.
What questions do you have about these data sets? What are you still curious about?
- I don’t have any questions now, but I’m curious to see if my opinion changes over time.
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(author_id, conversation_id, created_at, id, text)Add & Relocate Columns
Finally, since we are interested in comparing the sentiment of NGSS tweets with CSSS 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 “ngss”:
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: 911,173 × 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 911,163 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_1 <- ss_tweets %>%
unnest_tokens(output = word, input = text, token = "tweets")## Using `to_lower = TRUE` with `token = 'tweets'` may not preserve URLs.ss_tokens_1## # A tibble: 874,238 × 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… @cat…
## 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 1609854356 2021-01-02 00:49:28 13451697062071… 1345… 😄
## 10 ccss 1249594897113513985 2021-01-02 00:40:05 13451533915976… 1345… @hom…
## # … with 874,228 more rowsHow many observations were our original
ss_tweetsdata frame?- 35233 observations
How many observations are there now? Why the difference?
- 874,238 observations.
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_1 %>%
count(word, sort = TRUE)## # A tibble: 74,235 × 2
## word n
## <chr> <int>
## 1 common 26665
## 2 core 26470
## 3 the 25818
## 4 to 20478
## 5 and 15552
## 6 of 13106
## 7 a 12472
## 8 math 11788
## 9 is 11562
## 10 in 10076
## # … with 74,225 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_1,
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: 73,596 × 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 73,586 more rowsYour Turn ⤵
How many unique tokens are in our data tidied text?
- 73,596 (as indicated by the number of rows)
How many times does the word “math” occur in our set of tweets?
- 11,788 times
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: 73,593 × 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 73,583 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 to 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!
ngss_top_tokens <- ss_tidy_tweets %>%
filter(standards == "ngss") %>%
count(word, sort = TRUE) %>%
top_n(50)## Selecting by nwordcloud2(ngss_top_tokens)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 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.
- From Chapter 2, “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.”
Why should we be cautious when using and interpreting them?
- There are some limitations; sentiment lexicons could be less accurate depending on what is being measured (Jane Austen novels); they also do not contain every English word and may not be intuitive enough to understand things like sarcasm and how that effects negative/positive ranking. As well, the size could impact the score.
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 "@BURDVNK @Kam… 2021-01-21 17:53:31 7.86e17 1.35e18 1.35e18 Twitte…
## 2 "@NickAdamsinU… 2021-05-08 12:00:25 1.27e18 1.39e18 1.39e18 Twitte…
## 3 "@benblack1234… 2021-04-25 07:09:17 2.89e 9 1.39e18 1.39e18 Twitte…
## 4 "@bogle_chad @… 2021-04-17 18:25:11 1.25e18 1.38e18 1.38e18 Twitte…
## 5 "@J1mmie @Bill… 2021-02-01 12:44:37 4.05e 7 1.36e18 1.36e18 Twitte…
## 6 "Common Core M… 2021-02-24 00:43:33 1.29e18 1.36e18 1.36e18 Twitte…
## 7 "@lumberjake20… 2021-03-17 05:41:21 3.89e 7 1.37e18 1.37e18 Twitte…
## 8 "@QTRResearch … 2021-01-28 14:50:29 1.32e18 1.35e18 1.35e18 Twitte…
## 9 "@_RedQueenEff… 2021-02-02 17:04:33 1.28e 9 1.36e18 1.36e18 Twitte…
## 10 "@CarmenGrandd… 2021-02-11 13:59:46 1.12e 9 1.36e18 1.36e18 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 @BURDVNK @KamVTV The future of government-run(public) schools. Common Core at its finest.
## 2 @NickAdamsinUSA Understand this was originally packaged as common core. Dumb them down first then brainwash
## 3 @benblack1234 @Rians_Hopeless @MalloryGates14 @MikeMooreDO All the people complaining about common core were made fun of for their way of learning math. The old method, before both new methods, was great when dealing with bases other than ten. How often does it come up IRL? Mostly never. Be more open minded y'all\n\nhttps://t.co/ZHtPCSzjm2
## 4 @bogle_chad @BenjaminERogers @kenobi_baby @rweingarten We need ALL NEW TEACHERS THAT WILL WORK FOR OUR CHILDREN NOT THE WOKE LEFT TEACHING THEM BS LIKE SEX CHANGE AND CRITICAL RACE THEORY AND COMMIN CORE MATH.THAT DING DONG THAT POSTED THIS 115% CRAP PROBABLY HAD COMMON CORE.
## 5 @J1mmie @BillStrohler @ananavarro Common Core is meant to address the lack of logic and critical thinking instilled at a young age. But many people rail against it, not realizing that it will take some time to see the results of the new methods.
## 6 Common Core Math.. https://t.co/51iIabssu3
## 7 @lumberjake20 @RepMTG What did they teach in school?\n\nThat Common Core Syllabus ?
## 8 @QTRResearch Common core math. Bill Gates and the teachers union should be proud. Meanwhile, the leaders of other countries just verified how weak we are.
## 9 @_RedQueenEffect @PirateSteveYar @DovahChim @erupterupt @MrAndyNgo Is that supposed to be some kind of common core rebuttal? 🤣
## 10 @CarmenGranddau1 @MollyJongFast Propaganda units used to be part of state curricula in English. That is, it was until we had to switch to Common Core, and then it got taken out.
## 11 @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
## 12 @thisabellelam cc algebra actually makes a lot of sense ! it’s the elementary school common core that’s difficult ;-; i’ll never forget when a 6 year old stared at me like i was an idiot for not knowing what a stem and leaf plot was 💀💀
## 13 @Marking365 @scrapFe @fraracci @janice4iowa @LauraRBelin @JeffShipley77 Common core is making kids dumber. We've stopped actually teaching kids the core skills they need, and instead are filling their heads with a lot of partisan crap.
## 14 @andosmith @defnoodles He's 16 and watched a movie from less than 3 years ago when he was 12.... must be common core math
## 15 I finally agree with common core math! 🤣🤣🤣 https://t.co/M9OOnFtHGp
## 16 @mikepompeo Like Obama’s Race To the Top & Common Core Weren’t Enough? Two decades of public education that dumbed down kids by progressives & uninvolved parents & their political correctness crap
## 17 @potatoslav @jacksonhinklle If the spending for healthcare is $4t and there’s 330m people, what math are you using to get to $5-$6k/per person? That’s over $12/person, M4A is magically cutting costs by $2t? Even common core math says you’re being duped......
## 18 @dirtydiscooo America has been being dumbed down since they started using Common Core in Schools
## 19 Tennessee Senate Passes Bill to Ban Common Core Textbooks https://t.co/l5mFhqnSEb
## 20 @X_opherus @poppycockguild1 @catturd2 Dominion + common core math = infinity
## 21 @SBakerMD Please! The dropout that forced Common Core down our throats. Now he’s a climate expert forcing chemical slop on us.
## 22 @michaelharriot He might only understand common core math 🤣🤣🤣 that new shit will have folks fuqed up.
## 23 @Seekthetruth101 This makes sense for sure. And also the reason I only liked classes like Math, Shop, Chemistry and P.E. (and detention apparently 🤷🤦) THANK GOD I wasn't taught that garbage Common Core CRAP the kids are now. Gym was gym. We actually had wood shop, and auto, and, and... 🙇🤦😥
## 24 Says the 'masterminds' of #commoncore: the mazed-system that leads to a purposed outcome=> frustration.\n#beinspiredandbreakthematrix\n°remove cursive\n°remove literacy \n°remove math\n#dumbingdownworks \nPeople will believe whatever the rich white folk tell'em! https://t.co/zm5NsrNBTr
## 25 @AugustWest_1969 Add to that Common Core..and here we are.😔
## 26 @abby4thepeople OMG, when my son came home and explained triangles with "common core" I about went to his school for a fist fight with his idiot teacher...\nTurns out, they aren't idiots... they have to teach "the curriculum"... \U{01f92c}😡\U{01f92c}😡
## 27 @LVCabChronicles Common core at its finest.
## 28 @GOP @PARISDENNARD In 2018, inflation was 2.44%. In 2021, FOMC is forecasting 2.4%. Is the GOP really this bad at math since Common Core?
## 29 @Tactical_review @auminer Thinking she used common core math!
## 30 @jeremymbarr Did you count?, Common core math?, Round up?, Round down?\nPlease #circlebacktome
## 31 @war_there He + his kevin kline looking ass wife are responsible for implementing Common Core
## 32 @TimSweeneyEpic This seems like it has the potential to become overwhelmingly complex. If people have trouble learning common core, I think learning a new, albeit superior way to calculate would be as well.\nThis makes me think of Elon Musk's Neuralink, and intelligence augmentation.
## 33 @jmar1357 It is tough. In my opinion, the common core math curriculum has actually made math harder.. but as a teacher, we have no choice but to teach it this way.
## 34 @TucsonMelissa Or find a group to create a home school micropod, and you can even find materials that are NOT common core. It’s time parents start using their “buying” power, and stop paying for schools to remain closed.
## 35 @NikkiHaley @EliseStefanik Confucius is like American universities,Goethe Institute,Alliance Francaise around the world.China bashing is useless. We need to invest in green new deal,infrastructure,common core and relevant curriculum. Appointing of judges is not a policy for development. Let’s compete & win
## 36 @nancysantanello @realstewpeters @AOC @mtgreenee @RepMTG Can you use one more hashtag. I don't think you tagged it enough. 🙄 ANYWAYS... You win the Dumb 🏆 of the day. ZERO. Count it, and not with common core math, ZERO officers died as a result of Jan. 6th. So check your facts because you were lied to. Now you look like an 🐴🍑.
## 37 PDF Download 8th Grade Common Core ELA (English Language Arts): Daily Practice Workbook | 300+ Practice Questions and Video Explanations | Common Core State Aligned | Argo Brothers >> https://t.co/GwZL3XHicj
## 38 @Truem_13 @godgunsglory78 @realDonaldTrump What's wrong with you.? Oh that's right, you're one of those who have been brainwashed with common core.
## 39 #Fraud?\neven #CommonCore #Math...\nThey call it #algebra & tell us they're #Arabic #numerals but it's #Vedic math,\nmeaning #scientific,\n#knowledge based,\nnot mere #computation.\n\nWe can tack fraud onto plenty western governments #scheme,\nnot just math.\n\n#HistoryGate #NewChronology
## 40 @CommonSenseEd Ever wanted to show your students how to identify grade-level sight words? Originally designed for kindergartners, this lesson can be adapted for any grade level.\n\n#commoncore #technology #letters #sightwords #googleslides #kindergarten\n\nhttps://t.co/k5gZv73tL7
## 41 @ChrisCerrone15 It’s also why I joined & felt we did so much good. But for similar reasons I left the Opt Out groups. I never said anything but 2 women, whose names you’d know, threatened to come to my house with a knife. All over Astorino. It’s when I left. Common Core was not a reason to unite
## 42 @BSXA @JrzGrly @kirstiealley You used common core math to get that answer, I bet!
## 43 @lovin_william @timburchett Common Core Standards were abandoned because Tennesseans (among others) lacked confidence that their children could meet those standards.\n\nWe, in New York, lacked confidence in Tennesseans as well.\n\nSo all you have left, in Tennessee, are the Burchett standards.\n\nCongratulations! https://t.co/35VW79GoHk
## 44 @AmericanLOnline Common core math for ya
## 45 @jamestallevi @staceykate1973 Common Core mathematics on full display!
## 46 @loyal_dustin @gravitiesgone @BlessssYouGod @JoeBiden 133 million registered voters cast 160 million votes. Is that a common core figure because to me that don't add up.
## 47 @RyanMc23 8th grade? You should feel good about yourself.\n\nThanks to common core, I can't help my daughter with 1st grade problems. https://t.co/Kf0iHAdfdt
## 48 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
## 49 @justmenopot Common Core math! 🤣 https://t.co/ppkKg6YQve
## 50 Review Online:\n☞ https://t.co/SItaNGaVoJ\nLooking for 【Integrating Young Adult Literature through the Common Core Standards】 Download Mobi\n▶ by Rachel L. Wadham
## 51 Make it part of Common Core aka Common Enemy 🤷♀️ https://t.co/xUleAEE86g
## 52 The weekly shiur for parshas Emor BOX #1202, a shiur about Common Core on BOX #1203 (951) 262-3714. In Israel 079 934 1421 then 1 0544 & 1 0545 respectively .
## 53 @sarahmcpants @natebargatze I was never a math guy to start with; honestly if you were any good at it, I was convinced you were a sorcerer of sorts. Then I saw common core math, and figured the asshole who invented it by a rogue wizard possessed by dark magic.
## 54 @tedlieu @GOPLeader What’s the problem? She is correct. I guess Ted & the Democrats want to keep dumbing down the People with BS like Common Core.
## 55 @KunsanAirBase @sadboyhere @7thAF @USForcesKorea @PACAF @INDOPACOM Common Core math???? https://t.co/k6JS1qTLca
## 56 @JimHawk67327371 Common Core Math Class at the Berenstain Bears. How many republican mail-in votes can be diverted from the American pie?
## 57 @SwankThink It’s common core liberal brainwashing of our children
## 58 @miles_commodore Common core is ridiculous.
## 59 @Ever_Vidana It wasn’t that we wanted the vote challenged as much as delayed for a forensic audit. Even common core math \U{01f9ee} didn’t add up. Why was everyone afraid of a really good audit? It just doesn’t make sense.
## 60 @TheAcephalist @21WIRE Have a read up about "Common Core" educational standards amd Bill Gates's involvement in that.
## 61 @Bonniestillhere @PumaTPG @rationalnik @fwe1991 @TwinkieAngry @JMore247 @ADouble777 @jokersbythedzn @DurtyDancer1 @Daniell85958850 @ECRoberts3 @DevinDowney1 @EvilAlice_666 Dat common core edumacation I tells ya...
## 62 @cleverclue @ButterflyMcGrew @BrandonBretl @JessPish You sound like someone who hasn’t taught public K-12 under the Common Core.
## 63 @QuarantinedCoof Common Core is bullshit\nJust do regular math you fucking weirdos.
## 64 Get educated about your schools! E-Learning showed parents the absurdity of school curriculum. There is none! Remember Common Core, (Socialism). Teachers are not making these choices, your state governments created over-the-top standards. That is ALL that districts have to follow
## 65 Fuck, I'm having to defend common core in my team meeting this morning. It's so kids can learn compartmentalization and how step by step processing works. Also when someone does everything in their head, you can't teach them where things went sideways if they get a wrong answer.
## 66 @DonaldJTrumpJr DID WARREN GRADUATE WITH HONORS FROM A DEMOCRATIC COMMON CORE COLLEGE???\n\nI THINK HE DID THAT & CHEATED WITH HIS SOLENTEK SKYWAY SYSTEM TOO\n\nPROBABLY HACKING ALOT WITH HIS NSA COMPUTER SYSTEM \n\nDO THE MATH ON OVER 1800 OPERATIONS NATIONWIDE \n\nSOLENTEK $450,000
## 67 @H_MitchellPhoto Common core math😜
## 68 @dialmformolly @TheeSmartman @piptiegirl @TomIannitti Is there a Class in\nCommon Core Syllabus called -\nCommon Sense ? \U{01f9d0}
## 69 @JoshuaTorrey I've been using Singapore Math -- which Common Core actually mimics in many respects -- and the whole experience with that, seeing just how well it works, has been humbling.
## 70 @RepDanBishop @Bellamari8mazz along with "Common Core" please.
## 71 @alperinan @larmanius @clairecmc $26.6 billion in 2020, but you failed to acknowledge the $48.2 billion NY received in federal aid. Even a common core, libshit moron can understand this deficit!
## 72 @WeAreTeachers Ever want to show students how to add and subtract using the number zero up to the sum of five? Originally designed for kindergartners, this lesson can be adapted for any grade level.\n\n#commoncore #technology #letters #googleslides #kindergarten\n\nhttps://t.co/rBqSaelo8e
## 73 "The NABC is excited to evolve our relationship with CoPeace, with whom we share many common core values."\n\nThe NABC college basketball awards show is this Friday, read about our partnership and the awards presented by CoPeace: https://t.co/QJYb5HE1wP\n\n#InspireChange #BCorp https://t.co/bnWOxhqTuI
## 74 @BuranGeoff @Abyssal_Squid @Destiny25251210 @JamieJames107 @NickJudkins2 @RyanWes66596026 @1320KD @POTUS Common core math at its finest… 😂😂😂
## 75 @Trumpeteer14 @BrianKirkland5 Common core?
## 76 Cookie Monster would be shamed into stopping eating cookies and stuck with veggies and soy; Elmo would be forced to change his gender; The Count would have to count with Common Core; and Garbage Monster would also get no financial help - it's for illegals from Land of MakeBelieve
## 77 More funding doesn't solve the issue in itself. Government schools here in Kentucky have DOUBLE the money that the typical private school does, and the private schools still outperform academically overall.\n\nBut either way, I'm not particularly a fan of common core. https://t.co/9pqxPTEu7S
## 78 @quackkist Standards like Common Core leave locals to decide HOW to teach! Teachers have ALWAYS adapted to learners using 'one size fits all' textbooks & classrooms!\nWHY do ppl think CC is 'one size fits all'?
## 79 @isabellarileyus I've had children in both. Albeit at different times. Was pleased with the public school for the first few years. Something snapped with common core. Been down hill ever since. We r all but done now but if I had it to do over, I'd have pulled out and home schooled.
## 80 Just a friendly reminder: Common Core Math is for people looking for more drama in their lives.
## 81 @axios @MirandaCosgrove @GirlsWhoCode @reshmasaujani @IfThenSheCan COMMON CORE MADE KIR KIDS STUPID
## 82 Download our FREE letter sound and handwriting practice worksheets from our growing resource library: https://t.co/ssiFd3oCJ3\n\nCommon Core alignment: CCSS.ELA-LITERACY.RF.K.1.D\n\n💚 Be every child's most loved English teacher!\n\n#blendedlearning #lockdownlearning https://t.co/51s3FiEVLW
## 83 @PMadridShow This is a strange mindset. I have spent my whole life encouraging my children and grandchildren to succeed and excel. This will cause more harm then good in the long run. Wasn’t California one of the states who pushed common core?
## 84 @EditsByMare Half the fan base ... what the actual common core fukking math are they using to make that work out !? 🤔🤦♀️
## 85 I actually thought this was a prison, until I read the tweet and it said school.\n\nBut....maybe in a way this is a prison. A prison where common core lives on, kids are told the LGBTQ life style is ok, American heroes are looked down upon, health food forced, & Socialism is normal https://t.co/uNz91LTPrd
## 86 An analysis of "the sad adventure of Gates’ purchase of public education policy via the Common Core." @JanResseger @tomloveless99 @gatesfoundation #edpolicy https://t.co/S0JxfJONjW
## 87 @BarbMcQuade HE THEN FROZE THE SWIMMING POOLS, SO HE COULD ICE SKATE ON THEM\n\nWHAT COLLEGE DID HE GRADUATE FROM??\n\n WAS IT A COMMON CORE COLLEGE ??\n\nMY BROTHER IS A RETIRED USN SEAL TEAM COMMANDER & I TRAINED TO BECOME ONE A LONG TIME AGO \n\nYOU HAVE TO BE DELICATE WITH THIS
## 88 Seen the MARJORY SAVES THE EVERGLADES Educator Guide? It's aligned to Common Core Standards & approved by Friends of the Everglades--founded by Marjory Stoneman Douglas 2 protect this unique & endangered ecosystem.https://t.co/0FOj5hHqrA\n#kidlit #nature #WomenInSTEM #WHM #teacher https://t.co/dr4BAO7GKS
## 89 @j_a155 You could also label the puck as “My 3rd graders common core math”
## 90 this whole thread is proof that common core aint shit https://t.co/WSShQHYUHJ
## 91 @kirstiealley Common core economics. Right....🤔
## 92 @itsurmumlad @706Liam @SlanderSponge @megnotastallion @kunbuglin @riougly @spectatorindex Me too! Omg! Crazy shit. \nCommon Core! \nI don’t have no problems with ya but Liam is a hoot and the other one who said Columbia was in Africa. Good times!
## 93 @erikaheidewald Isn't this what's being taught as 'common core" now? /Gen
## 94 @MJackshiite There are NO parentheses in this example, you can’t assume that.\nAny number times 0 is always zero.\nI see a lot of Common Core Answers 😂😂😂😂😂😂
## 95 I don’t know about Common Core but there are a lot of kids going back to school who now know how to load a dishwasher, start a load of laundry, and unclog a toilet and parents everywhere are here for it.
## 96 @ohgodmarii @shOoObz In Oregon there's a grant where if you granduate and go directly to community college you get free tuition so I wanna do my common core education there to free up some stuff for University. Probably LCC or PCC for right now! Then I'll filter into OSU most likely.
## 97 @Neeters614 @michaeljknowles what's wrong with common core?
## 98 @StanTradingMan That’s not how common core works Stan lol
## 99 Common core education teaching us that black people were only in Africa in early civilization and the white man brought us here is literally meant to be little us. We were EVERYWHERE
## 100 @Noahpinion If Fox News is trying to generate outrage from a random school board member, you can all but guarantee they're strawmanning the proposal. Remember Common Core freak outs?
## 101 Highlights include:\n\n"That must be some women's math"\n"Looks like some liberal c******king common core math."
## 102 #CommonCore #CommonSense Knowledge is power site:Is this #compact style of writing #common or unique as editors say https://t.co/sxW9RHgWzm
## 103 PDF Download Regents High School English Language Arts (Common Core) Exam Flashcard Study System: Regents Test Practice Questions and Review for the New York Regen => https://t.co/qfzkQWoVoo
## 104 Great description of the Jan 6th morons. But also Trump's party members in general. This is what decades of school cutbacks and common-core does. https://t.co/JBiI44A26l
## 105 ➥ https://t.co/eOhWe9zDsC\n✧EBOOK Download\nPeter Pan (Graphic Revolve: Common Core Editions) by J.M. Barrie & Fern Cano ✧\n∞ status ready ∞
## 106 @sirena_lamar @FoxNews You’re stupid, and wrong. Sorry you got raised on common core, dweeb.
## 107 @drscottjensen I could be wrong, but last time I looked, the World [Common] Core Curriculum in our schools is a UN curriculum of global re-education and indoctrination, based on the writings of theosophist, Alice A. Bailey. Not certain that option is much better for our children. Just sayin'.
## 108 @betsey_burgin @RachelKasten @courtneymilan YOU knew it was metaphorical because you’re an adult; 7th graders in the age of common core (read: might never have had a poetry unit before) need a lot more help in understanding metaphors, especially extended ones.
## 109 @stillgray Bill Gates funded and almost single-handedly got Common Core implemented in our schools.
## 110 [Download] Kindle Summer Learning HeadStart, Grade 3 to 4: Fun Activities Plus Math, Reading, and Language Workbooks: Bridge to Success with Common Core Aligned Resources and Workbooks => https://t.co/CFhN4LNgSk
## 111 @ElijahSchaffer @LUVofCountry Common Core math.
## 112 @FenixAmmunition "Who died the most"="victory"? Is this common core math?
## 113 @RayGQue I went to grade school in the 90s but my kids do common core so im not even sure math actually exists
## 114 @tightrolltony @QuestMalloy @deray Your school doesn’t use common core? Cuz when I was teaching I was drilling supporting facts erryday!!
## 115 @beebopanana @KatiePavlich Taxes to school= $$ sent to unions = $$ kickbacks to politicians. Common core math.
## 116 @theangiestanton This equation is super hard.. I guess it’s that new common core math ;)
## 117 @EPoe187 @CHSommers Common core was not common enough.
## 118 @KeepCalmPersist @lindyli @patriottakes 2/3 for impeachment has existed LONG before Common Core existed.
## 119 @PDfh7gk @Joshua_one_life @uscensusbureau As a parent my experience with common core = a whole bunch of steps to say 1+1=2. I know there is more to it than that. It probably will turn our kids into math geniuses. But for someone who went to school in 80’s and 90’s it’s confusing.
## 120 @Kevin_McKernan I would have thought that after the wreckage wrought on the US education system from Common Core, we would have politely started shunning Gates from public life. His ties to Epstein, I believe, were after Epstein plead guilty. It does not speak well of Gates either way.
## 121 @tdotfish @DAkacki That's always how I've done math and it's not something I knew was taught. I would have done so much better with a common core curriculum
## 122 @Mrtdogg No needle AND she didn't push the plunger! "Good for thee, but not for me"!! I've heard Gates children are not vaccinated nor are they taught Common Core.
## 123 @RepCawthorn Pelosi got the common core education concerning science...
## 124 Helped lil sis with her long division . I was hype because idk what they teach these days . That common core is still bullshit to me.
## 125 @talesddc @NandovMovies You understand 1400 + 600 is 2000 right or does Biden have to learn common core to show you.
## 126 @RuleBrexitannia Its common core & the dumbing down of education thats the problem. I urge parents to educate their own kids.
## 127 End — end Common Core is dead. They’re a great chance for tremendous economic prosperity in the deal was dead. Every decision on trade, absolutely destroyed. To return home and my various other generals, you know what they do is they cut their currency. States within 10 years.
## 128 Terrence I think it’s called common core math and now we know just how common it is!!!!! https://t.co/lIrOZLqVzO
## 129 PDF Download Robin Hood (Graphic Revolve: Common Core Editions) >> https://t.co/toQtjZlFOW
## 130 This bill was vetoed in 2013 cuz someone mistakenly told the gov it was common core.\n\nOur stds deserve that the adults in charge must review the #txed essential knowledge & skills to ensure our children have a chance to learn them w/i one school year.\n@KingForTexas #hb424 #txlege https://t.co/HcsXK7TDQp
## 131 @uhhhgerry @OfSinfulGvrl @gagsheaux dumb is what we call a substitute for idiotic, but your common core knowledge has only taught you so much, keep going you are truly getting there \nhttps://t.co/oHDgHjJw2y
## 132 @LawrenceMuir1 For home school for just 1st grade, I taught as much common core math as I could. He'll be back in regular school for 2nd likely, so it was important to teach him their way.
## 133 @AngelaGraceLOU @ToddHarding_17 Exactly!! Common Core sucks for parents who have learn new ways they don't recognize, but it's really helping the kids with their achievement levels!\n\nIt's really unfortunate that negative parental attitudes toward the new ways sometimes hold kids back.
## 134 @Danny__Diamonds @putnam_israel @ScottBaio Yet he couldn't even pull a dozen sheep together at his events or get 1000 viewers on his live streams. Lemme guess, the common core got ya, huh....? SAD
## 135 All businesses have some common CRM so they can rip off their clients more effectively\nSo it is oftentimes a hypocrisy to see so many banks existing while they have common core\nIt should be called uno fascism
## 136 @raemickrae @megynkelly Did I miss the news stories on the tremendous gains of math scores by those learning "Common Core" math? Did they suddenly surpass the kids taking "Singapore Math" like my kids learned at their charter school?
## 137 Mega Food Park, Ladhowal is equipped with modern infrastructure and provides an excellent opportunity for entrepreneurs to leverage the benefits of basic enabling and common core processing facilities. https://t.co/qgkoXn4YkW
## 138 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
## 139 6. At the end of SHS1, Students shall write a Common Core Exams into SHS 2.\n\n7. At SHS 2, students will now have to select either a career related programme which include, vocational and technical programmes or a high school Diploma programmes such as science, busines, arts.
## 140 Schools Test-Drive Common Core - https://t.co/PHxW4LPN9J
## 141 @Unrigged9 @8BrianVogel4 @ziggystardad @1Quetzalcoatl1 @Vickie627 @StrokeyStratfo1 @JeffreySHarper1 @4ever_patriot @ruxcytbl @PATRIQT_1776 @CaptainTeag @furcopalypse @PoliticsPot @MrChingonE @mathmomma24 @Stevent0000000 @PogueMoran @RickySi16087724 @BSHerrle @Ihonestlydont19 @_Kel_Varnsen_ @terryloohoo @JonSmith922 @Forseti_Pazzo @laylow88861429 @Jim65783 @medwoman1 @moose57579 @MistressRedWasp @Lilpoo404 @henriziolkowski @epitomeof3 @JDW714 @Merry_Hippie @ahrehead @jdd2169 @Lastplace_champ @besosinthehouse @DanielP58009529 @Patrici76267702 @PaulStetson13 @chrisg409ubc @Emma34770971 @DanieIGregg @LadyOfTheOcean1 @thedemorats @therealmcteag @UnimpressedTX @QuidRises @BPeske Easy to figure out it was a steal just look at past presidential election do a little math (No Common Core Math) you will see the problem numbers don't add up \nAnother way go State by State get total number of registered voters you will see for yourself numbers don't add up
## 142 @jadntrumpebooks Statement from the clueless. Common Core suffered from the same problems Reagan found in 1980s, bad implementation, poor training & curricula!
## 143 @ScottAdamsSays Common Core math strikes again\n\nIsrael Population 9 million 10% = 900k\nUSA Population 331 million 10% = 33M\n\nAre we bad or just bigger?
## 144 @Harley_Chic47 @allohio1974 @lainyj12 Thank Gates for that. Common Core BS
## 145 Daily Radio Commentary with Ed Martin 🦅\n\nCommon Core Omits Cursive Writing https://t.co/V5LTcuGDWJ https://t.co/bRJF44t4C1
## 146 @QrisMidnytrdr @elisamich0422 If your IQ is over 80, this 'common core' science doesn't apply to you.
## 147 @CBSScottWhite Common Core grading!!
## 148 Now can we all collectively agree that their idiocy is a direct result of Common Core Math? #SAVETHECHILDREN https://t.co/ruWq84m9jq
## 149 Arkansas Mother Obliterates Common Core in 4 Minutes! https://t.co/RKeOTq9gx1 via @YouTube \n\nOVOMIT'S 'COMMIE CORE' CONFOUNDING OUR CHILDREN /GRANDCHILDREN & TAKING AWAY THEIR SELF-ESTEEM TO DO BASIC, SIMPLE MATH !!
## 150 @SanfordLJohnson Ha, the folks who I know who use it were not in school in the common core era.
## 151 Is there an interactive flowchart version of the Common Core math standards that can assist with vertical planning? If not, how much do we need to pay someone to make this? #mtbos #ITeachMath
## 152 @RichardGrenell @alexstamos they are scared that we are awake. They want to dumb us down like the use of the common core.
## 153 @julie_kelly2 7 seriously? Is this the result of common core math? I know these people have issues but even the most brainwashed have a smidgen of intellect.
## 154 @rednecktigger @METRO25DISH @MikeD_530 @seanhannity I wasn't aware common core was on trial sparky, but if there is any doubt that the jury heard anything the defense can use it as an appeal reason as I'm sure you are aware.
## 155 If you would like a break from things, watch @natebargatze on Netflix. The Greatest Average American is a great time. The Common Core math stuff really hit home with me. Loved it!
## 156 @charlotte_wMHA It’s for my Learning and Cognition course. The first prompt was about Common Core State Standards...so, naturally it got quite heated lol what course are you currently teaching with discussion prompts?
## 157 @LeaksSodez @AuplesRBX @LeaksSodez Maybe you should actually know what we were building and asking questions before leaking it, common core.
## 158 [Download] Kindle Common Core Achieve, GED Exercise Book Social Studies (Ccss for Adult Ed) => https://t.co/3w53UsOkOZ
## 159 @brianjoralvarez That’s common core math
## 160 my daughter just say “6 is halfway to 10” common core math right there
## 161 @SpeakerPelosi It’ll cost the American people $6000 to get $1400. That’s democrat common core math for you.
## 162 Perhaps the time isn't right because the Obama administration, the Fordham Institute, and all the governors who dumped Common Core on us already said we were getting "better tests" and paid consultants millions for tests they now say are failures. https://t.co/8ZELfDNZkP https://t.co/aIyvXigz9z https://t.co/BdM8gqtADM
## 163 @itisyaya Common Core? Do you learn to subtract with CC math?
## 164 #summer is quickly approaching! Our #STEM Fundamentals #curriculum is fun, academically rigorous and aligns to NGSS, ISTE, CSTA and Common Core ELA Literacy and Math!\n\nDownload the curriculum overview and set up your free demo here: https://t.co/ERImiuCiiS https://t.co/WDvRRIJpxo
## 165 But Biden wouldn't lie 🤣 he was just using that common core math good grief he is an epic fail!\n\nBiden's bizarre Amtrak story doesn't add up https://t.co/JiQiNf9HTS #FoxNews
## 166 Did you know that Ohio is among the states listed where schools follow the Common Core initiative?\nCheck out this AMAC Article: Leftist Indoctrination In America’s Schools -- https://t.co/F7GUwKoHUl
## 167 @AlKatori85 @gunpolicy Must have learned the Common Core version of the Constitution?
## 168 @Long_Tailed_Tit @MalloryGates14 @MikeMooreDO Everyone I know who does math in their head uses common core methods. Some of us can't do it in our heads, common core makes no sense and we need it written down in columns. Why can't they teach both?
## 169 @readingtips4u Common Core doesn't teach history either!! Ask schools instead of blaming!!!
## 170 @SWGaspar @johncardillo Common Core with #feelingz
## 171 Umm, is this why we now have Common Core math? 🙄 https://t.co/McV1QWcHZ5
## 172 @GavinNewsom If 5 million vaccines have been administered how is that 1 in 10 people with a population of 40 million? Common Core Math?
## 173 @TimBenzPGH I blame it on Common Core Math!!!
## 174 @We_Have_Risen @BamaGirlzRbest I thought common core math was bad, it makes more sense than this https://t.co/L0DXjXytes
## 175 @catturd2 by Common core math standards at a buck a book profit\n\nu are now a millionaire. Congrats. :-$ #bling
## 176 I mean...I think that element is in the Common Core State Standards under Language Arts and Writing...but in most school situations, students don’t write lab reports in English/Language Arts, y’know what I mean? #games4ed
## 177 @BRotoFFTim Agreed. I hear everyone complaint about it and I get to teach the way I want. Still feel bad for the students. The idea of common core isn’t necessarily bad it’s the implementation
## 178 @ShellyWasko @DeAngelisCorey @JudyForAZ PPl should determine what choices exist but settle for what publishers & others decide to offer.\nPPL should be aware of what 1980s DeptEd study found & help shape ed process & content! That is what standards like Common Core do to strengthen local control!
## 179 Meanwhile, Watson argued that Common Core would intentionally create students unprepared for STEM careers.\n\n"That means we're gonna have to bring other people in the country to do those careers. See where we're going? It's troubling." 🙄 4/ https://t.co/DcXGCnsHYK
## 180 @MattsIdeaShop @realDailyWire wut's that 👆like common core math or sumthin'🙃❓
## 181 @AdamLefkoe This is what happens when kids learn math through common core 🤦🏻♂️
## 182 Primary teachers needed for excellent British schools in Bahrain, Bishkek and Kuwait! job with ...: Experience teaching one of the following curricula: UK, American Common Core, Australian, New Zealand, Canadian, Irish, South African, or IB. NQTs ... https://t.co/4d0nNL7AWM
## 183 @ConceptualJames We are fighting against Common Core, the 1619 Project, and Critical Race Theory in the New Hampshire State Legislature. We've even included a provision in the budget to prevent tax dollars from being used for this. https://t.co/X6vzWj7bEK
## 184 Is that common core math or dominion-smartmatic counting ? https://t.co/DI2pwdEPLv
## 185 @MadBarbi More common core training? I’m out.
## 186 @Natalia01Mateo @ALGhammer Yup. And the survivors, armed with a "common core" education, will be too dumb to read anyway...
## 187 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
## 188 @TDavid_21 @HeythereNosh 🤣🤣 hard to follow along. I’ll start using common core
## 189 🇺🇸Biden want taxpayers to support even more education which is primarily leftist indoctrination.\n Our children are not being educated with common core, critical race theory, and white children learning they are 'privileged'. '\nhttps://t.co/WKa5YKoweF
## 190 Climate literacy should be a part of the common core curriculum. https://t.co/5rK8xWDlnB
## 191 @engineers_feed Common core math
## 192 @DanPriceSeattle This must be some new common core BS. By your math, assuming Musk worked 40hrs a week for 52 weeks he made 40 trillion. Might want to go read the tax code too. Not that you'd understand it.
## 193 !Regents High School English Language Arts (Common Core) Exam Flashcard Study System uses repetitive methods of study to teach you how to break apart and quickly solve difficult test questions on the Regents. Study after study has show
## 194 @Frankgr87839318 @HammalyHassan @TPum01 @washingtonpost Common Core doesn't really cover social studies except for a small bit in ELA. There are no CCSS Social Studies standards.
## 195 @CaliCat20 @BigJebBos Common core
## 196 @RNCResearch What a bumbling fool. \n\nHe must have learn using common core math
## 197 @ArchieStannumb1 Seriously, whatever. I use YouTube for fitness videos, fashion history blogs, common core math help for kids, and real estate videos for houses I can’t afford. You know, serious things. Not some trifling channel released after someone got a huge ovation at a big fundraiser.
## 198 @judyannaggie Is she doing the Common Core math?
## 199 @vjeannek @AmericaRising17 @rweingarten Common core teachers
## 200 @tomspettigue @rmb3cker @briprice661 @OliviaTroye @RaheemKassam @natsechobbyist @kaitlancollins @AlisonDeLuca No it’s not. It’s a Constitutional republic. Maybe not in your common core fictional politics class, but in the founding documents. #BringBackCivicsClasses
## 201 @LiveEvolveLife They were probs attempting common core math\U{01f92f}
## 202 Maithili shares a common core vocabulary with other Indo-Aryan languages such as Hindi and Nepali. As a matter of fact, over 90% of Modern Maithili vocabulary is Indo-Aryan. \n#मैथिली_शिक्षा_गियर्सन_अनुसार
## 203 @ChuckCallesto As long as the TX Education Agency doesn't go around it with nonsense - like they did with Common Core.
## 204 @mikepompeo Cancel culture has been in our schools since Gates pushed Common Core down our throats over a decade ago. DeVos was part of the plan in Michigan. Look into the fam paying for Lisa Lyons campaign who was appointed head of Ed comm w an Agricultural degree.😡
## 205 @AlanWindham @EdMorrissey @shipwreckedcrew You are not very bright. Perhaps common core remedial could help you out.
## 206 @EddieMcClintock @caroljsroth It's hard since the implementation of common core math. https://t.co/CJPfsqXMIH
## 207 @_parasocial Is spelling the as 5he a Common Core thing?
## 208 @Pennys4Vegas @ElCortezLV They had to change it cause common core math made it to difficult to count 🤣
## 209 But remember - teachers seem to prefer doing things the hard way. Look only to common core math to see what I mean. 🙄🙄 Whoever came up with that crap really needs some schoolin' in common sense and the hell with common core. Ain't nothing common about it, it's common BS. https://t.co/voo1rqPVbA
## 210 @glg1101 @cpa_kay @gburton @azcentral We are in trouble if they believe in Common Core Math that 2+2=5... serious trouble! \nHahahahaha
## 211 @nellz69420 @JefferyxBball Common core type beat
## 212 @RobRitchiee @otakushinjikun @MarkLFeinberg @BernieSanders what's so bad about common core?
## 213 @heckyessica 1776 + 400... minus Common Core... yeah, seems right. Fact Check: True Enough.
## 214 The deadline to register for the @calwater #CalWaterH2OChallenge is less than 2 weeks away! Tackle a local water issue with your students in this class competition that's NGSS and Common Core State Standards-aligned! \n\nRegister @ https://t.co/tWAw1PZZq2
## 215 @KelBelleKAG Common core math \nNo one gets less than a 60....
## 216 This needs to be added to common core.\nhttps://t.co/n89FKl9OjA
## 217 @bethroessler @CNBC @kaylatausche RTTT did not specify which standards. More points were given to cooperate with other states. Could have used any standards. States seemed reluctant to share their own standards so Common Core was only standards shared.\nhttps://t.co/Fncg66azqv
## 218 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
## 219 Why Common Core failed https://t.co/gmUanf0ezK via @BrookingsInst ✅ I was one of original “trainers” for Ca. English Lang. Arts, K-12 Model Curr. Standards. As Curriculum Consultant for Sacto. County Office of Education, before Principalship. @CMSPrincipal #edchat End testing!
## 220 @GovEvers 40%, is that common core math?
## 221 Common Core Math????? https://t.co/PYwNkbb16y
## 222 @voxdotcom One thing is for sure...it isn't Common Core Math!
## 223 @trixxxen @YWNReporter It has to be common core math in bidens America. This looks so fake...almost like false flag fake. \nBut you are 100% correct, math is not mathing.
## 224 @corvetteguy59 They tried the same thing with that 'common core' commie crap
## 225 @TysonGardin #formativechat \nA2:Getting rid of high-stakes testing is 1/2 the battle. The other 1/2 is getting rid of common core, which led to high-stakes testing in the 1st place. Establish broad conceptual themes, identify components. Set up a curriculum & evaluation to identify progress.
## 226 @Elenaforever13 @lpmitchellrtr 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
## 227 @jasonparker83 @CSexton25 @GovBillLee They phased out the name, but still the Tennessee Academic standards root the common core and the TNReady test are still aligned to common core standards
## 228 @jamie2181 Common Core was funded by Bill Gates + Passed by Barack Huessein Obama
## 229 I could teach your dumbest child common core math in Chinese as long as they’re under 5. It’s not about their smarts or mine. All you need is an undeveloped brain and decently designed materials.
## 230 @worldnetdaily Because the charade of "common core" Bill also came up with is an atrocity. Throw a little racism in.
## 231 We need to make education better in America also. In every continent. The international common core standards messed up a lot. Just like each continent is unique geoproximally with resources the students are too. We need different standards before we all merge later.
## 232 @IngrahamAngle I can control who my children are around when homeschooled. Public schools are not all that. Common core sucks,kids are brats teachers are not professional like before they treated thier position as a job dressed like it as well not like today. Wannabe best friends.🙄
## 233 @feck_me_running Todays Common Core educated masses know.
## 234 PDF Download Prentice Hall Literature: Common Core Edition -> https://t.co/aTcEqpD3Ry
## 235 @TpT_Official Ever wanted to show your students how to identify grade-level sight words? Originally designed for kindergartners, this lesson can be adapted for any grade level.\n\n#commoncore #technology #letters #sightwords #googleslides #kindergarten\n\nhttps://t.co/k5gZv73tL7
## 236 @EROTHCJ5 Common core, dude. Totally works
## 237 we need internet literacy courses in all schools as part of common core education i have strong feelings about this https://t.co/3lllFPNgBv
## 238 @EuphorbiaZ @dailycallout @CBS_Herridge Statistics says otherwise but we don't expect people with liberal arts degrees who only had common-core math to know this. https://t.co/K5pSUSnBiQ
## 239 @SiriusXMNASCAR @DGodfatherMoody Happy Birthday Dave!! Common core math must of been used to figure out your age haha. I know probably not funny. I hope you have a great day!! Have fun at Bristol this weekend!
## 240 @TheOracle201823 @mchooyah There are only 133 million voters in the USA, Trump got 74 million in the lowest counts, I know you probably took common core math, but how did Biden get 80 million? You are the one that has been deceived. I know you think you won something, but liberals destroy liberals first.
## 241 @AlexDanvers2017 I retired in 2015 (after 36 years of service) as soon as I maxed out my CA pension benefit. A good decision in hindsight. Common Core implementation, continuing district merry go round w/ software apps, & now Covid & virtual teaching! No way! You 2 deserve a nice weekend! Enjoy.
## 242 @CKeck13 I did finally drag myself around the block with all the good grace of an eight year old learning common core math. https://t.co/hDjtLyQjUa
## 243 @DeAngelisCorey PUBLIC SCHOOL SUCKS!!!COMPLETE DUMB-DOWN INDOCTRINATION .... JUST LOOK AT COMMON CORE !! BELOW POSTED BY A PUBLIC SCHOOL TEACHER ON SOCIAL MEDIA... “google” ?!?!? THIS SPEAKS FOR ITSELF!! (This person had 11th graders watch “cnn” EVERYDAY for the first 15 minutes of class! 🤢🤢 https://t.co/ESUCJ5OxAl
## 244 CVC Phonics Word Houses | Consonant, Vowel, Consonant Sounds | Phonics Worksheet | Grades K-1 | No Preparation Packet | Common Core Aligned by Cameron Frank Products for $1.97 https://t.co/apMW8ArKex
## 245 @hepbot @mr1492 @LoriBaker7 @DeAngelisCorey This isn't like buying a new tv or car. Also, look at Common core. Gates foundation's push to improving schools, RAND institute study found it was a mistake, basically Gates was like, oops.
## 246 I just lost a few brain cells reading this tweet. I didn't know common core math was this bad. 😬 https://t.co/LKdV0AAln9
## 247 When are conservatives going to start canceling common core books and liberal education?
## 248 @JuliusGoat How dare you even MENTION that 4+0=4; we’re only interested in finding “bipartisan compromise” where 4 ==2+2 😫😫😫 GODDAMNIT LIBERALS YOU & YOUR COMMON CORE MATH 😭 https://t.co/SDRgPuggL6
## 249 #CommonCore, #SoCalledWhiteSupremacy, #Transgender, #FakeNews, #Hollywood, #BigPharma, #GMOs, looks like they already won. SAD, really SAD. https://t.co/6P6hMiNWLu
## 250 @KBoomhauer @unrealdeveloper I think it follows his EO regarding K-12 education.\nAka, undoing common core/ indoctrination
## 251 Here's a great video to use at the beginning of your #NUITEQSnowflake lesson activity to teach students more about relative pronouns. Do you want more free #CommonCore aligned videos? Follow the NUITEQ Snowflake content channel on YouTube. https://t.co/VgJR5fVhHD
## 252 @johncardillo What will happen:\n\nPoor kids will not learn any math.\nKids with money will go to tutoring after school to learn proper math and the divide will grow. \n\nRight now, kids with $ learn proper math during after-school tutoring, after learning common core @ school.
## 253 @EdwardianOsirio @ACTBrigitte @FBI Did Trump keep his promise to eliminate Common Core? Did Trump keep his promise to remove all undocumented immigrants? Did Trump keep his promise to eliminate gun-free zones at schools and military bases? Did Trump keep his promise to stop the AT&T Time Warner Merger?
## 254 @thisistechtoday Or one of those Common Core Math memes that used to float around! 🤣
## 255 @TufftedSquirrel @ChoiVGK I like this math!!! Is this the new common core the kids are learning?
## 256 @SparkyPatriot Common core
## 257 @JohnCLeer This dude using common core math or smth https://t.co/jivMUl9qgd
## 258 @PhadingDark Hi Phae....are we having a communication issue with a common core, under educated, science doesn't matter only my opinion and feelings are facts complete moron again?...😁
## 259 Common Core for civics... actually worse than Common Core if that were even possible (it is, at least Common Core didn't advocate a particular political ideology). Here is a piece by Stanley Kurtz discussing the Educating for American Democracy Initiative.\nhttps://t.co/DNO30ZVNyQ
## 260 They said Common Core Math was Better. Now look at what you did. https://t.co/08LaK0PUDN
## 261 @catturd2 Common Core
## 262 @AubreyJ47046403 @tr00p3RR Wtf is common core
## 263 @thehill Look at all these Biden cultists defending Biden’s lies with common core mathematics. Y’all Biden cultists are treating Biden like the Trump cultists treated Trump!
## 264 @ladderofjacob23 @nojumper Uh wouldn’t that be well over a mill per person or did you use common core to figure this out?
## 265 @laurenboebert It's Common Core New Math. No worries though. We're all gonna croak soon enough
## 266 @trekkerteach12 Yes— glad you find it useful. I was kind of shocked at how recent some of the white papers were— knew that CAP had been in the mix on Race To The Top/Common Core, etc but they’re still plugging away
## 267 @zeframmann @TheOnion Nah, Common Core was the worst.
## 268 Common Core Math... https://t.co/T4MFjqHWiV
## 269 @aubrey_huff @Harvard What if dog actually spelled Cat?\nAnswer: common core.
## 270 LGBT activists, AND Democrats in general, are the byproduct of Common Core Biology... https://t.co/r5GHj5XuoJ
## 271 @wagatwe tbh yes,,,, common core for me literally was do things the way you’re taught or get points off,,, other methods were wrong so everything was just memorizing,, maybe its changed because i went to school while the were first starting it but probably not
## 272 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
## 273 @cai_limaa Common core is just what my brain does. I was common coring before common core was common coring. Call me .... hard core 😉
## 274 @catturd2 Are you using common core Math? You will get a different answer if you do.
## 275 @Milos_2020 @catturd2 I believe it’s either common core math, or might have been calculated using the same software/ machines we use for elections.
## 276 @roeguardino @nypost It’s 3 but using NYS’s common core it comes out to 4
## 277 i hate the common core i hate the common core
## 278 @PaulRey37196061 @thedailybeast Red States would welcome a cut in public education. They way they think - if the feds cut 7%, states like MS or FL would cut 15% and then blame Common Core for why their schools suck.
## 279 @PlayingWithDice We’ve been playing around with a few things for some assessment pieces and checklists, but they might adapt to standards based ... feel free to DM if you’d like. We aren’t common core up here but the structure might align?
## 280 @ZeeLeafs What exactly is common core? Is that a music genre?
## 281 Common Core https://t.co/uF5YBjFBYC
## 282 PDF Download Common Core Basics, Reading Core Subject Module (Ccss for Adult Ed) -> https://t.co/7bsJ85l4k5
## 283 @AOC How does 9% of $1.9 Trillion equate to a covid relief bill when the other 91% is completely unrelated to covid relief? Are you guys doing common core math again?
## 284 @FOX2News Is this common core math? If they only announced 21 deaths, I don't know how they included 51 deaths from vital records review.
## 285 @barstoolsports Still better than common core.
## 286 @stem_feed @engineers_feed Is it what they teach with the common core?
## 287 Schools Test-Drive Common Core - https://t.co/7IWbQMdDMY
## 288 @acls9_9 Common core math?\nJust throwing that out there
## 289 @lawyer4laws @CABeck1961 That’s common core math for ya
## 290 @BernieSanders So that's why the government started teaching common core math.\n\nIt's easier for government to plunder the private sector when they don't understand how the economy works and keep getting plastered with class warfare propaganda.
## 291 @catturd2 Hey, that’s the common core way of doing math. Impressive!
## 292 @natbaker @realJosephRich Common core math?
## 293 @SnackSizeAsh Also the way a teacher or school grades is independent of the common core. I agree wholeheartedly that in general, students are graded too softly and it doesn't align with what students can do academically. That's why standards based grading works well.
## 294 haven't touched this game in over 3 years, and i come back and get this from a common core crystal.....lol. https://t.co/CPhbq5sf1N
## 295 When end of days homeless people preach \n\n#nyse 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/hbc7hy9vHb
## 296 @PosterLs I get it tbh, most femboys were raised with common core, I wouldn't like that math either.
## 297 @EricaJoy @code4QueerNCute Well there goes that common core educational ideology surfacing. There is and never will be 1000%. Only someone who thinks there is would back the things you went full tard about. If product or service is human then back that business. Geeze!
## 298 @kerryjeanlister @gerrybrooksprin I see you don’t understand \n“ common core “ , this may help but doubtful, it’s Bullshit https://t.co/wVCJxr4QFZ
## 299 @NickKnudsenUS @GOPSenate Worlds already forgotten your over hyped psyop bullshit.\n\nunfortunately for you Americans you rank very low on the people whom are worth listening to list very very low,\n\nbunch of common core educated communists,
## 300 @JeffTheGK Jeff, there's a vast group of ppl in the US who have bought into the concept that 2+2 =5. We're watching it play out. You haven't bought it. Don't entertain these common core infantile minds. Keep up the good work.
## 301 @OANN I guess you people never thought a Union could hold thousands of kids hostages to beef up their pension plan.\nThey make 100 K and only work 9 months a year never mind holidays and all weekend's. All that money gets you common core outcome based education LGBTQ agenda. ta daaaa
## 302 @LekeAlder True that 👆🏿(but they share a common core - Nigeria).
## 303 @NotBarron1946 Seems to me they have control, ex. mandatory vaccines, common core, many more to list
## 304 @1BrianThompson @neontaster Gotta love that common core education, eh?
## 305 @CWBChicago Common core math
## 306 @melisalw Apparently proper handwriting is not a part of the common core.
## 307 @JoeGoodberry @LndsPatterson Very true... they don’t teach us common core math in Bounty Hunting school.. you’ll have to excuse me 😂
## 308 The Most #Effective Exercises and Review #CommonCore # Math Questions.\n\nhttps://t.co/kBkDKoTjao
## 309 @russvought We don’t have common core in Florida anymore
## 310 Common Core in Action: 10 Visual Literacy Strategies https://t.co/9YXCVhKpnQ
## 311 When democrats use common core math to justify social distancing 😂🤣 https://t.co/Tota45Y8a8 https://t.co/Gs5873GhQR
## 312 Texas used to choose textbooks or many other states. Wonder if they still do. Nebraska did NOT adopt Texas books, though. Nebraska also did not adopt Common Core. #IngrahamAngle
## 313 @BernardKerik @KimWeav60019701 Gibberish Joe 🤣🤣🤣🤣🤣🤣🤣 common core honor student \U{01f92a}😜 https://t.co/p5mEQDP1m7
## 314 @DeAngelisCorey @JudyForAZ Maybe it’s Common Core math...but GPS (Gilbert) just axed a bunch of teachers. Less students=less teachers. If teachers would demand that the boards fund from the classroom up, there would be plenty of $$$. Instead, there is bloat in upper admin.
## 315 @JoshuaTorrey One of the ed profs at my fundamentalist undergrad college had worked on common core math standards for some state--Ohio maybe?--and was *very* annoyed by how so many conservatives would misrepresent them.
## 316 @JackPosobiec you downplay Gates by calling him a mere scientist. He’s also the greatest educator (common core & the new ct math), greatest farmer (largest farmland owner in US now) & greatest nutritionist (synthetic lab made meat). In his mind which has somehow become our world, he knows all
## 317 @PopeJudasV @MalloryGates14 @MikeMooreDO Wait but why is the common core method better
## 318 @daveonuevo @POTUS That would appear to be the people who were “educated” in Democratic cities, via common core math, so..... \U{01f928}
## 319 And damn are they good at distracting us from this fact. Sometimes I think the common core and no Child left behind are all about that distraction. https://t.co/0XUpHXLLya
## 320 @THR The number 3 is racist. The number 3 MUST be cancelled along with two plus one. This will also be in line with the Common Core agenda.
## 321 @brobert545 I needed that chuckle. Common core math on parade.
## 322 @ContraWarBlog @ToombsToni I'm not explaining anything to a common core coastal loser like you.
## 323 @AllanForsyth @SweatyGardener What is this common core?!
## 324 @MsTuckwiller Part of the common core curriculum?
## 325 @BP_Rising common core teaches the wrong history. the history that enables their tyranny.
## 326 Common Core is academic Career Pathways is CTE but it seems a lot like he went to the RCU website without knowing 90% of that staff voted for him. https://t.co/s0mlcewTID
## 327 @LesJohnsonHrvat @DanteWillBe @MomOf3blessed_1 @gatewaypundit @tan123 He must be doing common core math.
## 328 Duane Roen discusses the top-down model of assessment that has dominated K-12 education and is currently being promoted by the national https://t.co/d2tYzuMRLL #commoncore
## 329 Did @POTUS just fail at basic math.. he got that fresh #commoncore
## 330 @QTRResearch I do agree with you that the last few unrests, including WSB is all share the common core of socioeconomics. I am not necessarily in agreement with the causes, I think that is much more complex.
## 331 @Getaclue77 Ugh common core is down in COS too!! We will be homeschooling as well.
## 332 If I get 10 new followers, then I lose 20. Common Core math? 🤔
## 333 @tevan2864 Decades of Common Core dumming down our offspring.
## 334 @LionelMedia What's a pronoun?\n\nI was educated by common core.
## 335 @RosaWal90673631 Congress can refrain from offering those hefty grants to blackmail schools into teaching woke anti-American & anti Civil Rights ideology. This is Common Core/Race to the Top, 2.0.
## 336 @ClaudeL1979 @StanielSorensen @bridgettyh @TheRealAndrew_ Naw, they do Common Core math: 2 + 2 = purple because penguins don't wear pants 🤣
## 337 “I’m convinced the Common Core will be remembered … as an unfortunate detour on the road to improving public schools,” writes @stevepeha in a letter to @rickhess99. #EWOpinion #RHSU @rickhess99 @AEIeducation https://t.co/eFCkjxY3ui
## 338 Obama s illegal executive order on immigration protect the Second Amendment end Common Core rebuild the country who may not have positively enhanced the novelty.
## 339 Me and Chris when he was in school! 😂 Common core whooped my tail end! https://t.co/u6AE7S3VNB
## 340 @poke_m0m @neonally_ $250,000 per individual when married and filing jointly is far less than $400,000. (That’s the marriage penalty)\n\nDo you even read or are you having trouble with your Common Core number crunching?
## 341 This wouldn’t even cover one month of rent in a crappy 2 bedroom apartment where I live. I assume their econometrician learned common core math. https://t.co/SVtSEPlWFc
## 342 Tom Loveless: Why Common Core Failed https://t.co/7CrF9M49s4 via @dianeravitch
## 343 @WeAreTeachers Ever wanted to show your students how to identify grade-level sight words? Originally designed for kindergartners, this lesson can be adapted for any grade level.\n\n#commoncore #technology #letters #sightwords #googleslides #kindergarten\n\nhttps://t.co/Zz0ZutqlzD
## 344 @brianjoralvarez 20. Common core?
## 345 @mtngma @gvlob @LakesFirearmsTr @SebGorka Common core should not be in the conversation. But I agree with your main points.
## 346 @EdReformWatcher @spencerlatu Father Hesburgh and Clifton Wharton were peddling it in the early 70’s. \n\nThis is a long game. \n\n@Woodyboy2020 🎯 \n\nhttps://t.co/ur3yB5qgLZ https://t.co/F9nF5UEJWN
## 347 @emtothea No, which is why I hired a tutor for my kid. I embrace my limitations! Plus, Common Core sucks!
## 348 @DeanBrowningPA Say what? Is that common core math?
## 349 @DonaldJTrumpJr No wonder Biden likes Common Core math.
## 350 DEFCON 3 [HARD CUT] Unusual Cloud activity: Stargate Command adopting Common Core at latest by this quarter.
## 351 @SusanStJames3_ Common core sustainable medical care system dont care about health. It only give treatment/care, not cure !\nAddiction is good for the economy and drug cartel and drug manufacturers. It just do no good to health, Roman empire been doing this for 1000s of year\U{01f971}🤧
## 352 Common core math. \n\nThank Barry for this! https://t.co/tNbrnePdCI
## 353 @jairaadiaz i feel you but i just hate how over the years they’ve made common core more difficult
## 354 @catturd2 Well, Dems do love Common Core.
## 355 @_Rotey @BreitbartNews If you use common core math, the equation is still being written out. Give them 2 more weeks and we may get 2 weeks closer.
## 356 Blame it on common core math! https://t.co/kj5jVV0BLE
## 357 Implementing the Common Core doesn’t mean educators have to abandon SEL—in fact, just the opposite might be true.\n\n#SEND #SENDParnets #EHCP #SENCO #Education\nhttps://t.co/ParXvqrcBo
## 358 @Kenny74957788 @DrKarlynB That's precisely what this is you illiterate mouthbreathing nimrod. \n\n"They want to abuse our kids by dumming down to common core." \n\nI'm confident you couldn't describe what the common core is if you looked it up on wikipedia and had one of your relatives read it to you.
## 359 @BaldnGold Truly is Mark. Now the big question, how to fit 26 desks with 3ft distance. The Common Core question of the week. 🤣
## 360 @politicalmath @SaraRC1 A lot of the common core math lessons I see are often the tricks I use to do computations in my head. People who screech about common core math are those who think 1/4# burgers are bigger than 1/3# burgers.
## 361 @RyanAFournier Common core math
## 362 @needabetternam3 I’m pissed trump didn’t remove common core like he claimed. Like that would have been dope as fuck
## 363 If You Liked Common Core, You’re Going To Love Joe Biden’s Anti-American Civics Project\nhttps://t.co/3j9y4wz6Ab
## 364 @BigFaceKev1 @SF_Gamerbabe11 Only two possible outcomes. The vote for accountability was bipartisan. This isn't common core math. Stop trying too hard. Q wouldn't like it.
## 365 @thomasa56 @TimRunsHisMouth @KurtSchlichter Rushed to implement common core all makes sense now.
## 366 @BradGerstman We are the "Empire State," so I don't care about the standards in other states. If testing required knowledge is "teaching to the test" then you have a perverse sense of what constitutes a quality education. Killed Common Core. Now, killing the Regents exams. Sad. Really sad.
## 367 @Chinchillazllla Teaching homeschool for my second grader has just been... ugh. Such weird requirements for 2nd grade social studies in common core.
## 368 @MsAvaArmstrong Pearson Pub, common core...look it up. Obama set them up the got a sweet book deal. hmmm
## 369 @EduNeverDies #EducationNeverDies \nA3: Having schools w/different themes that would appeal to a S's interests. Offering school choice so S's can go to a school that appeals to him/her. Building curriculum not on Common Core but using Student-centered approaches. Stimulate the mind & heart.
## 370 @MissRobinAustin That’s the new common core math!!
## 371 @ClassCastPod 1. Not every state does common core. 2. Rural schools don’t offer as much because they don’t have the money. I know just looking in my region there are wide varieties between districts. Tech integration and availability. Voctech offerings. Etc. Big differences
## 372 @Cernovich Same non college grad guy who ruined education with Common Core now is a health expert🙄 He hates humans. RUN from @BillGates recommenders.
## 373 A lot of info packed into this and the previous geopolitical reports of X22 this week.\n1. Rioting now in preparation of election audits. \n2. Antifa = Brown Shirts.\n3. California declaring math is racist, created by the same people who created Common Core,
## 374 @HeyItsJordhan Stop this is so simple. Is this common core wizardry?!
## 375 I suggest a new #CommonCore #Education: (S)science, (T)Technology, (E)Engineering, (E)English, (M)Math, (P)Physical Education, (H)History, (A)Art, (R)Reading, (M)Music all of this to start as early as a child can learn.
## 376 @IlhanMN And cause over 1.4 million to lose their jobs... how is that a win? Common core math?
## 377 @128blackjack @SenWarren @abbydphillip You are delusional, apparently you only know common core math. You do realize that out of that 330 million half are under 18 and not eligible to vote. D**b#ss. Also not all people vote.
## 378 @AngelRTalk @beingbuzzy @TractusB @DonnyReila @divinedre11 Is this that common core stuff my mom friends yell about? 😂
## 379 @catturd2 85 masks + 4 shots = 0 covid protection (common core covid math)
## 380 @jonathonstack Common core teaching no Common sense whatsoever.\nThat doesn't even make sense to people who never owned their business. \nThe indoctrination of our kids is completely out of control!!
## 381 @msengupta @kevin_astro7 Gonna disagree a bit: common core is a methodology for teaching, not necessarily what's taught. Teacher friends of mine have said that it's not as bad as it's made out to be. It's different. \n\nAlso: good scientific education should start before secondary education.
## 382 @rationalwalk You did not use common core math, carry the one or move ANY decimal places! #fakenews
## 383 @1Krohmer13 @BuckBond4 @PamelaGeller Common core math
## 384 @freginold_JS Then you should get a refund on your Common Core education.
## 385 How the Failure of the Common Core Looked From the Ground (Opinion) https://t.co/ZmKCmIyJs2 https://t.co/dAmAkyXwon
## 386 @kushibo @DonaldJTrumpJr Even if population was quadrupled, she won an election, then didnt represent them. I know common core makes it hard to understand what majority of voters mean, thats why you think the exact population natters
## 387 LOL. wat \n\n“Today’s common core kindergarten math standards include construct viable arguments and critique the reasoning of others...” https://t.co/B3w2O1fWMG
## 388 @SHAPE_NATO When playing games do not use common core it's fuzzy math???? https://t.co/ynCk7e3jDL
## 389 @AmyGore3 @palan57 Nothing like what the Common Core has done for corporations. Can't even touch it! Don't bother getting back to us because it is the truth. The Common Core was the stupidest legislation that both B and O signed off on. Easily duped but now teachers how slanted the system is.
## 390 @ValaAfshar @elonmusk 2 + 2 = 5, common core math, current
## 391 In another big step nowhere good in education, Biden picks a big supporter of Common Core and reforminess. ofhttps://www.edweek.org/policy-politics/biden-taps-ex-obama-aide-roberto-rodriguez-for-key-education-department-job/2021/04
## 392 @cmmteach Concepts: what is a function, linear functions, rate of change \n\nProcedures: equivalency, simplifying expressions, solving equations \n\nBut Virginia doesn't use common core so the actual answers for her might be different
## 393 @parlertakes Is this common core math?
## 394 @CalemAnnk I always thought common core math was when you took something like\n\n77+28 and broke it down to 75+25+5\n\nBoy was I MISTAKEN.\n\nHoly shit their stuff is insane.
## 395 @catturd2 Common core
## 396 2. All students in JHS 1 - SHS 1 shall run a Common Core Programme (CCP) which comprises of 9 subjects; Maths, Languages, Science, RME (stand-alone subject), Physical and Health Education (not examinable), Career Technology, Social Studies, Computing and Creative Art and Design
## 397 Common core https://t.co/0MoOvqT14s
## 398 🙃AGAIN..??..COMMON CORE..🙃2+2=5..??( 1984\U{01f978}).....🙃 https://t.co/XRAoFMpyJ6
## 399 @aces2269 @MikeSweeden @DanODonnellShow @TheFrancescaF @CNN Common core math at its finest!! \U{01f92a}🤣
## 400 Facts that are not part of the Common Core Curriculum https://t.co/ctKyMdkXac
## 401 @CindyKendrick11 Common core algorithm
## 402 Something wrong with that math...Common Core Student? https://t.co/JlasiVKOMC
## 403 I just learned kids are being taught Common Core Math across the country.. \n\nWhoever created this.. \n\nYou’re an idiot. https://t.co/Fs9H7bGW2i
## 404 It's common core , duh 🙄 https://t.co/JLVyNVR7PC
## 405 So, We the People sit on our duffs for 4 years paying taxes to Pakistan, China, Russia. ISIS, Venezuela, & in 2024 your vote is discarded like int 2020? Every registered voter in the US voted twice?? Do the Math unless you learned common core math? https://t.co/jSAV3CZaxs
## 406 Imagine comparing actual homeschooling with being forced to manage Zoom class attendance and help with common core subjects. \n\nCherry picking data is not proof that homeschooling is less than public school. Public schools are a joke and a sham. https://t.co/Aa24cYJlCZ
## 407 Now we know who’s responsible for Common Core. Accidentally of course....they’re not smart enough to come up with this on purpose! https://t.co/vrdAXOANLU
## 408 Hourly is the dumbing down of our country, must be the masks, or pharma drugs, or depleted brain cells from Common Core! Either way it’s a Travesty
## 409 @alicekeeler @youcubed Do you teach mathematics???? Do you know what the common core standards are in California????
## 410 @JesseKellyDC It's cool man. It happens to me from time to time. I blame it on my public edumacation. At least when I was in school common core didn't exist & correct answers in math weren't racist. @Bane_Returns @AtomicDog602
## 411 PEMDAS says it’s 1, Common Core will tell you there’s a 5 or 7 in the answer and the internet will do it incorrectly, subtract it by the right answer and say that’s what it is. https://t.co/kfgHZYJNde
## 412 Participation or results? Tell us in the comments below. Read both sides of the story and vote on 4b. https://t.co/dSBTqdOTYA\n#education #parenting #commoncore #trophy #participation #learning #school #motivation #students #study #covid #student #children #teacher #science https://t.co/oQ1N0ROzRM
## 413 @POTUS You took office please show us how many votes you received. Things are not adding up unless you were common core raised.
## 414 It's that new common core. No one knows. https://t.co/esisDuvurf
## 415 these people will get angry about Common Core math and prefer box and whisker plots. THEYRE THE SAME THING! ITS LITERALLY THE EXACT SAME THING
## 416 @MizzRothko @fit__feminist @ShotgunRain5746 This comes up about once a week or more in homeschool groups. People complaining about "common core" without knowing what they are actually complaining about.
## 417 These are common core values shared by many. List three of your core values then learn more at https://t.co/IlKJo34TqT https://t.co/zolgJplHKD
## 418 @haveaconcern That must be common core math
## 419 We used to make jokes about common core, now none of the numbers add up not in the election or minimum wage. This is all being done to collapse America! https://t.co/tiJFALhLr5
## 420 @MoMoBagholder @divinedre11 People hate on the common core but it does simplify long math. Momo you will revisit second grade soon enough. Go open minded. #Estimation
## 421 (2/2)\n3. Blind faith in the benevolence of a govt/(under-funded) 🏫 system that claims to protect children’s welfare, but still purposely dumbs them down w/ Common Core & poisons them w/ unhealthy, GMO food, when the data is in on both.\n4. Legality—it violates the #NurembergCode.
## 422 @TheAliceSmith @rangermonk1 Sounds the common core version. Too long.
## 423 Common Core? How about you teach some Common Sense
## 424 @DeAngelisCorey That’s what happens when kids teach themselves. He’ll look at common core. Seriously, Wtaf is that bs
## 425 @heelvsbabyface @LadyGravemaster It's a loathsome idea that is clearly the intellectual groundwork for genocide. I just wonder what stops New York State from banning ideas they don't like like capitalism, but then they already banned teaching arithmetic with common core, so...
## 426 @JohnFugelsang Vaginas into another vagina??? Is that Common Core Biology?
## 427 @jefffcarr @RickUcchino Nick Krall’s answers are as clouded as common core math
## 428 Way to lower the bar. It’s worse than common core and no child left behind. \n\nWhen you teach to the middle average is all you will get. https://t.co/xIfeCKKEbY
## 429 Example of common core education\n👇👇 Society has been dumbed down https://t.co/bjoviPf15d
## 430 @Academy_Change shares the report that illustrates how systems thinking can be applied to large-scale change\ninitiatives, toward whole system transformation. #UsingEvidenceBetter #ResearchSnapshot https://t.co/FMEc1m2zHv
## 431 I was excited to find this useful rubric for thinking about student skill in the standards for mathematical practice. @geoffkrall \nhttps://t.co/dJA9VBSb17
## 432 The common core curriculum is the death of the desire to learn
## 433 @lawyer4laws @Cara_TXZEAL Arguably, science, made up of theories and proofs is not absolute (altho the pro-shutdown side falsely declares their "science" infallible). I'm more concerned at the complete disregard for basic math, which is absolute. Then again maybe they're confused by common core math? https://t.co/kplipDViHo
## 434 @safeschoolsny @NYCHealthCommr @DrJayVarma Common Core math
## 435 Looks like they are still using common core math in counting elections. 2+2 is not 5.
## 436 @dogleaps That would never go over there. No one else exists but them.😡 They're still fighting common core and are making headway bc they've choked education to death. They blame their low scores on CC. Never seen so many ppl dead set against education.
## 437 @BernardKerik book deals...they alll use them as slush funds....research...try the co. given the common core contract who also sliced off a chunk for 44
## 438 President Biden, please, get rid on "Common Core" in our educational system, students are not learning anything from it. #JointAddress
## 439 @dcexaminer @BillGates Bill Gates was behind the failed Common Core education program. His ideas are not always fruitful.\n\nFacts don't work for him. https://t.co/iFUOQ97hYZ
## 440 She said "only with common core math" 🤣🤣🤣 She is 100% right. People are over his incompetent leadership which has turned the beautiful state of CA into a barely habitable cesspool that citizens & companies are leaving in record numbers. https://t.co/ZouAHx8X9D
## 441 For those #XenobladeChronicles2 fans out there. Common core, no stacks in my favor, less then 1% chance...I'm gonna (happy) cry myself to sleep now. For those who don't know about KOS-MOS...feel free to google how hard it is to get this beauty..\n\n#xenoblade2 #XenobladeChronicles https://t.co/q5vEyA2JbJ
## 442 @RepBoebert I doubt you can even explain common core math when will you start representing all constituents of CO3? #copolitics
## 443 .@guypbenson The real facts us census bureau 2020 stats 213mil LEGAL registered voters, 67% of whom voted=143mil LEGAL votes, trump got 74+mil, 3rd party 3mil 143-77=66mil LEGAL votes left for biden Please explain how he got 81mil, common core??????
## 444 @GovBillLee You wanna help the students? Then get rid of common core!! Bring back basic curriculum!! Stop dumbing down our kids!!
## 445 @LaxerJoe Maybe it was the beer or common core lol
## 446 @jhueser1439 @divinedre11 Common core¡!!!!!!
## 447 EBOOK Download Free Teaching STEM and Common Core with Mentor Texts: Collaborative Lesson Plans, K–5 -> https://t.co/PvyBHuKKXn
## 448 @garrygolden Do you mean common core? My kids are learning 4 different ways to arrive at the answer, more of a Swiss Army knife approach to numbers.
## 449 Check out our online reading and writing summer tutoring programs where we teach students to read complex academic English. #commoncore #english https://t.co/8sCE9h6LGG https://t.co/dCE4uXuc1W
## 450 @TG22110 Gotta love common core. Lol 😆\U{01f92f}Boom
## 451 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
## 452 @JennaEllisEsq Those must be the fake news numbers! Calculated with common core math ! Lol
## 453 [Read] Mobi 5th Grade Common Core Math: Daily Practice Workbook - Part II: Free Response | 1000+ Practice Questions and Video Explanations | Argo Brothers >> https://t.co/VVwOP0r25m
## 454 @DraganOrlich @trishie818 @noorchashm @CVSHealth @cvspharmacy @riteaid @DrWoodcockFDA @TuckerCarlson Child of common core math. 🤦♂️🤦♂️🤦♂️
## 455 @JackSillin it hurts my brain listening to her. I'm sure she grew up on common core math as well.
## 456 @livingtwig @RichBryant14 @The_Ghost_Rat @realDonaldTrump The Confederate Flag was for State’s rights. See you’ve swallowed the bait. Common core educated? 🤓
## 457 For just 15 dollars you can get 2 dollars. So this is the economy post common core mathematics. https://t.co/1VayNdu3qr https://t.co/Tl58riLHoY
## 458 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
## 459 Good grief....Pence sold out Gen Flynn, before that as governor he was for Common Core and every other thing typical RINOs support....he is the last of a dying breed...RINOs who campaigned as principled conservatives, he also certified most fraudulent election ever https://t.co/jrhO9pkpjz
## 460 @anyone_famous @timburchett No dice. What our education system has become thanks to common core needs to be fixed. The incoming freshman BEFORE the pandemic were the most unprepared for college with lowest entrance scores ever after having 4 full years of it in high school. You have a nice day sweetie!
## 461 @TheGeekCupboard If you think that's bad... lookup videos on America's "common core" math. You want something to break your brain? This is it. I got into an argument w/ my son's teacher when I provided him w/ algebraic formulae to do his fractions. He understood immediately, and completed work... https://t.co/jtuGp7ET3G
## 462 Hey remember that time all the Big Smart EduTech folks were 100% right about VR, or the death of print, or the death of libraries, or the death of classrooms, or standardized testing, or common core? \n\nNo? Cool. I’ve got a few MOOCs to sell you.
## 463 Common Core Math? https://t.co/E7nZJsxNz6
## 464 @Kevin_Maguire Religion can be a wonderful source of comfort to many. Most religions have common core themes and positive values. Adherents should focus on and celebrate this commonality, not squabble over the observance of the intricacies and differences.
## 465 PDF Download Common Core Math 4 Today, Grade 3: Daily Skill Practice (Common Core 4 Today) >> https://t.co/CHo3HwXfXO
## 466 @DailyCaller 😆😆 Uh, was this result by way of the common core method, or what?
## 467 Get Free:\n⇛ https://t.co/r9tGJUvEfy\nLooking for 【Common Core English Workbook: Grade 8 English】 Read Free\n▶ by Ace Academic Publishing
## 468 @JLF_comms @Bogenrim @MickPuck @sarahkendzior @POTUS @SenSchumer Common core votes\nDumbasses
## 469 @laurenboebert That common-core math gets them every time.
## 470 Only the people who were taught common core think 1/6 = 9/11
## 471 Common core democrats https://t.co/iPvuqL9fDE
## 472 @drunkenalpaca Because as educator, they don't actually have to teach. Think common core.
## 473 @crvnberrie Parents being confused by SIMPLE homework is why Europeans laughed at U.S. for decades!\nCommon Core math focus is on solving problems NOT being human calculators needing others to set up the problems. Just look at the panic caused by 'word problems'.
## 474 @absofabucorn @EdmJbg @1in5advocacy @dutchgirlusa @SKH245 @bellebeignet @EducateAll101 @IEPforParents @HabenGirma @alexanderrusso @moms4FAPE @ICARSBanRandS @MorrisonBeth Disabled kids are supposed to be exempted. My state only got an exemption because our state standards of learning testing already in place is far higher bars than common core. My autistic son doesn’t have to meet tje SOL benchmarks. That’s the case with most testing
## 475 @SalemStalkerera @maddow When did I state I don't believe Social Distancing or masks don't work? \n\n"From March 2020 to now, I haven't been sick for over 2 years." - Must be common core math. \n\n"the stupidity in you is strong." - That is textbook projection.
## 476 What if the modern populist revolt started with something innocuous, like Pluto’s demotion or common core math, and then just snowballed?
## 477 @Forbes Your math is wrong....$1400 X 156M = 218B\n\nWait, did you use common core math?
## 478 @EdB_Ohio @ZaidJilani Not really. Banning Common Core was a multi-state Republican priority; DeSantis was still concerned about it as late as last year.
## 479 @GlennKesslerWP Ummm, that would be Biden. What's a matter, common core got you stumped?
## 480 @RionNile @JoeBiden @WestJournalism Seems to agree with conclusions Reagan found in 1980s, LONG before Common Core.\nhttps://t.co/hqR8cdrAhA
## 481 @WatchtowerRome @Margare77396679 @RealMattCouch @Annakhait @DineshDSouza @JFlippo1327 @JosephJFlynn1 @ScottPresler @iheartmindy @DarlaShine @govkristinoem @LisaMarieBoothe It’s common core math. 🤣
## 482 Common core math is just demonic math
## 483 @joe172726kag Common core math!😎
## 484 @ilSharko Fuzzy math or common core math?
## 485 @TPeopledied @MarinaWhy1 @m4cgr3g0r @GOPChairwoman Lost 20 million Doses, more likely the morons in his Royal God kings administration are a product of common core, they cant even count to ten let alone 20 million.
## 486 bill gates promoting common core simultaneously both explains why it is so bad and why it was accepted so quickly
## 487 Georgia must’ve been using Common Core during the count.\nThanks, Obama. \n#RiggedElection2020 https://t.co/IfvCzlEBHh
## 488 Can't wait to read @tomloveless99 new book on Common Core. \nhttps://t.co/hZKShROKVW\n@rcraigen @manateespirit @erickalenze @tombennett71 @mayak46 @GaleMorrisonEd @smarterparrot @JohncattUSA
## 489 @teachertwit2 The ultimate reason for 'online learning' is what they did in the US with the Common Core, it's precisely the opposite to what it says on the can; the purpose is forcing an *individual trajectory* on each child, consigning the great majority to a scrap-heap.
## 490 @rhodabseed @ScottBaio Reagan found U.S. ed failing & producing generations of stupid kids (now adults)! Should focus on how to apply knowledge that is a major Common Core goal.\nTeachers & textbooks are struggling to create effective instruction to make it succeed!
## 491 @white_arrow_uk Called common core education n countless cultures,languages n people destroyed by it https://t.co/ZGXMj6ROaf
## 492 @Polymath_88 @shiteintrovert @KathrynAbernath @tycassidicloud1 @BlueAlaska2 @justinamash Private prisons are the result of over-emphasis on free market and privatization coming from many libertarians. \n\nThe curriculum does not come from the federal level. It comes from state standards and Common Core and is very decentralized. Get your facts straight.
## 493 @bleuciel1967 @davereaboi You just described Obama's common core to a T
## 494 Extra Bonus Fun Fact: Albeit slapdash and somewhat disjointed, Smooth felt that his tutoring technique was vindicated when one of his students blurted, “I didn’t know that math could be fun.”) #kids #students #school #math #volunteer #teacher #commoncore #classroom #proud #pride https://t.co/8Tp9DeKUHC
## 495 @realDonaldTrump @rickallen Well there is an argument against common core. \nPeople have no idea how to count anymore?\nManually count the ballots.
## 496 I have a PhD, but I am completely useless when my 3rd grader asks for math help. \n\n#funny #comedy #kids #jokes #humor #silly #laughs #laugh #mom #momlife #sahm #housewife #housewives #dc #jokes #meme #memes #comedian #like #lmao #commoncore #fun #love
## 497 @miles_commodore Nobody understands the convoluted nature of Common Core math. Instead of repairing the system that teaches this hogwash, Liberals just proclaim that "Math is Racist"
## 498 I PULLED HER ON A COMMON CORE CRYSTAL ON NIA IM LIKE LEVEL 20 WTF IS HAPPENINGGGJSIDBSKDNDXJ https://t.co/qsQeeKSm48
## 499 @RealKHiveQueenB More who don’t know math. This shows the necessity of Common Core. These poor individuals who were left behind.
## 500 No one feels bad for a common core math failure @ZachHalverson.\n@PowerTripKFAN \n@CoryCove \n@Chris_Hawkey \n@MeatSauce1
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## 97 {0, 0, 0, -2.1, 0, 0, 0}
## 98 {0, 0, 0, 0, 0, 0, 0, 0, 2.35}
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## 104 {1.55, 0, 0, 0, 0, 0, -0.65, 0, 0, 0, 2.55, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 105 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.5, 0}
## 106 {0, 0, 0, -2.4, 0, -2.1, -0.3, 0, 0, 0, 0, 0, 0, 0}
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## 111 {0, 0, 0, 0, 0}
## 112 {0, 0, -2.6, 0, 0, 0, 0, 0, 0, 0}
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## 116 {0, 0, 0, 0, 2.9, -0.4, 0, 0, 0, 0, 0, 0, 0, 0, 0.9}
## 117 {0, 0, 0, 0, 0, 0, 0, 0}
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## 121 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2.17835, 0, 0, 0, 0, 0}
## 122 {0, -0.6, 0, 0, 0, 0, 0, 0, 0, 0.95, 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}
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## 125 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 126 {0, 0, 0, 0, 0, 0, -0.5, 0, 0, 0, 0, 0, -1.7, 0, 0, 0, 0, 0, 0, 0, 0}
## 127 {0, 0, 0, 0, 0, 0, -3.3, 0, 0, 3.1, 1, 0, 0, 0, 0, 0, 0, 0, 0, -3.3, 0, 0, 0, 0, 0, -2.493, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.1, 0, 0, 0, 0, 0, 0}
## 128 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
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## 131 {0, 0, 0, -1.15, 0, 0, 0, 0, 0, 0, 0, -1.3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2.85, 0, 0, 0}
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## 133 {0, 0, 0, 0, 0, -0.75, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2.2395, 0, 0, 0, 0, 0, 0, 0, 0, -3.4395, 0, -4.44555, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
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## 136 {0, 0, 0, 0, -0.6, 0, 0, 0, 0, 0, 0, 1.693, 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}
## 137 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2.7, 1.8, 0, 0, 0, 0, 0, 1.6, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 138 {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}
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## 140 {0, 0, 0, 0, 0, 0}
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## 142 {0, 0, 0, 0, -1.5, 0, 0, -2.2, 0, 0, 0, -1.7, 0, 0, 0, 0, -2.5, 0, -2.1, 0, 0, 0}
## 143 {0, 0, 0, 0, -1.5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -2.5, 0, 0, 0}
## 144 {0, 0, 0, 1.5, 0, 0, 0, 0, 0, 0}
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## 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, 0, 0}
## 150 {0, 1.4, 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -0.4, 0, 0, 0, 0, 0, 0}
## 152 {0, 0, 0, 0, -1.9, 0, 0, 0, 0, 0, 0.3, 0, -2.3, 0, 0, 1.5, 0, 0, 0, 0, 0, 0}
## 153 {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, 3}
## 154 {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, 0.75, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.95, 0, 0, 0}
## 155 {0, 0, 0, 1.5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3.2, 0, 0, 0, 0, 3.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2.9, 0}
## 156 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2.62835, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 157 {0, 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, 1.1, 0}
## 159 {0, 0, 0, 0, 0}
## 160 {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}
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## 163 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
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## 165 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2.85, -3.3, 0, 0, 0, 0, -3.75, 0, -1.95, 0, 0, 0, 0, 0, 0, 0}
## 166 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 167 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
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## 169 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -2.2}
## 170 {0, 0, 0, 0, 0, 0}
## 171 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 172 {0, 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, -1.4, 0, 0, 0, 0, 0}
## 174 {0, 0, 0, 0, 0, 0, 0, 0, -2.5, 0, 0, 0, 0, 0, 0, 0}
## 175 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.9, 0, 0, 0, 0, 0, 2.4, 0, 0}
## 176 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 177 {0, 1.1, 0, 0, 0, -1.2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.3, 0, 0, -2.5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -2.5, 0, 0, 0}
## 178 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2.55, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2.25, 0, 0, 0, 0, 1.95, 0, 0}
## 179 {0, 0, -1.5, 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, -2.5, 0, 0, 0}
## 180 {0, 0, 0, 0, 1.5, 0, 0, 0, 0, 0}
## 181 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 182 {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, 0, 0}
## 183 {0, 0, 0, -1.5, 0, 0, 0, 0, 0, 0, 0, -1.3, 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}
## 184 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 185 {0, 0, 0, 0, 0, 0, 0}
## 186 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -2.3, 0, 0, 0}
## 187 {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}
## 188 {0, 0, 0, -0.4, 0, 0, 0, 0, 0, 0, 0, 0}
## 189 {0, 0.3, 0, 0, 1.7, 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, 1.9, 0, 0}
## 190 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 191 {0, 0, 0, 0}
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## 194 {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}
## 195 {0, 0, 0, 0}
## 196 {0, 0, 0, 0, -1.9, 0, 0, 0, 0, 0, 0, 0, 0}
## 197 {0, -0.7, 0, 0, 0, 0, 0, 1.1, 0, 0, 0, 0, 0, 0, 0, 1.7, 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, 1.3, 0, 0, 0, 0, 0}
## 198 {0, 0, 0, 0, 0, 0, 0, 0}
## 199 {0, 0, 0, 0, 0, 0}
## 200 {0, 0, 0, 0, 0, 0, 0, 0, -0.6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 201 {0, 0, 0, 0, 0, 0, 0, 0}
## 202 {0, 1.2, 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}
## 203 {0, 0, 0, 0, 0, 2.233, 0, 0, 0, 0, 0, 0, 0, -1.7, 0, 1.5, 0, 0, 0, 0, 0}
## 204 {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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 205 {0, 0, 0, 0, 0, 0, 0, -1.62282, 0, 0, 0, 0, 0, 1.7, 0, 0}
## 206 {0, 0, 0, -0.4, 0, 0, 0, 0, 0, 0, 0, 0}
## 207 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 208 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.5, 0, 0, 0}
## 209 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -0.6, 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, -5.4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 210 {0, 0, 0, 0, 0, 0, 0, -1.7, 0, 0, 0, 0, 0, 0, 0, 0, 0, -0.3, -1.7, 0}
## 211 {0, 0, 0, 0, 0, 0}
## 212 {0, 0, 0, 0, 0, 0, -2.793, 0, 0, 0}
## 213 {0, 0, 0, 0, 0, 0, 0, 1.2, 0, 0, 0, 0, 1.8, 0}
## 214 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 215 {0, 0, 0, 0, -1.2, 0, 0, 0, 0, 0, 0}
## 216 {0, 0, 0, 0, 0, 0, 0, 0, 0}
## 217 {0, 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, 0, 1.2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.4, 0}
## 218 {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}
## 219 {0, 0, 0, -2.3, 0, 0, 0, 0, 0, 0, 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}
## 220 {0, 0, 0, 0, 0, 0, 0}
## 221 {0, 0, 0, 0}
## 222 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 223 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.889175, 0, 0, -1.05, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 224 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.6}
## 225 {0, 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.8}
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## 227 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 228 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 229 {0, 0, 0, 0, -2.3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.184, 0, 0, 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, 0, 0, 0, 0, 0, -2.807, 0}
## 231 {0, 0, 0, 0, 0, 1.9, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.4, 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}
## 232 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.11, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.5, 0, 0, 1.1, 0, -1.11, 0, 0, 3.2, 2.1}
## 233 {0, 0, 0, 0, 0, 0, 0}
## 234 {0, 0, 0, 0, 0, 0, 0, 0, 1.1, 0}
## 235 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 236 {0, 0, 0, 0, 0, 0}
## 237 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2.3, 0, 0, 0, 0}
## 238 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 239 {0, 0, 2.7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, -1.406, 0, -1.406, 0, 0, 0, 3.1, 0, 0, 2.3, 0, 0, 0, 0}
## 240 {0, 0, 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -2.85, 0, 0, 0, 0, 0, 4.05, 0, 0, 0, -3.75, 0, 0}
## 241 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 1.9, 0, 0, 0, 0, 0, 0, 0, 0, 2.5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.2, 0, 0, 0, 0, 0, 1.8, 0, 2.2}
## 242 {0, 0, 0, 0, -0.9, 0, 0, 0, -1.9, 0, 0, 0, 1.9, 1.8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 243 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 244 {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}
## 245 {0, 0, 0, 0, 0, 0, -1.11, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.8, 0, 0, 0, 0, 0, 0, 0, 0, -1.4, 0, 0, 0, 1.5, 0}
## 246 {0, 0, -1.3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -2.5, 0, 0}
## 247 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 248 {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}
## 249 {0, 0, 0, 0, 0, 0, 0, 0, 1.5, 0, 0, 2.7, -2.833, 0, -3.126, 0}
## 250 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 251 {0, 0, 3.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.3, 0, 2.593, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 252 {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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 253 {0, 0, 0, 0, 0, 0, 0, 1.3, 0, 0, 0, 0, 0, 0, 0, 0, 1.3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.3, 0, -1.2, 0, 0, 0, 0, 0}
## 254 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 255 {0, 0, 0, 1.5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 256 {0, 0, 0}
## 257 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 258 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -0.074, 0, 0, 0, 0, 0, 0, 0, 0, -2.2, 0}
## 259 {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, 0, 0, 0, 0, 0, 0, 0, 0}
## 260 {0, 0, 0, 0, 0, 0, 1.9, 0, 0, 0, 0, 0, 0, 0}
## 261 {0, 0, 0}
## 262 {0, 0, -2.8, 0, 0, 0}
## 263 {0, 0, 0, 0, 0, 0, 0, 0, 0, -1.8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.5, 0, 0, 0, 0, 0}
## 264 {0, 0, 0, 0, 0, 0, 1.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 265 {0, 0, 0, 0, 0, 0, 0, 1.332, 0, 0, 0, 0, 0, 0, 0}
## 266 {0, 0.85, 1, 0, 0, 0, 0.95, 0, 0, 0, 0, -0.5035, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 267 {0, 0, -0.4, 0, 0, 0, 0, -3.1}
## 268 {0, 0, 0, 0}
## 269 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 270 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 271 {0, 0, 0.85, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.05, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 272 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 273 {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}
## 274 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 275 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 276 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 277 {0, -2.7, 0, 0, 0, 0, -2.7, 0, 0, 0}
## 278 {0, 0, 0, 0, 0, 2, 0, -1.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.1, 0, 0, 1.5, 0, 0, 0, 0, -1.1, 0, 0, 0, -1.4, 0, 0, 0, 0, 0, 0, -1.9}
## 279 {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, 3.45, 0, 0, 0, 0, 2.25, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 280 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 281 {0, 0, 0}
## 282 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.1, 0}
## 283 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2.1, 0, 0, 0, 0, 0, 0, 0, 0}
## 284 {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}
## 285 {0, 0, 1.9, 0, 0, 0}
## 286 {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}
## 289 {0, 0, 0, 0, 0, 0, 0, 0}
## 290 {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, 0, 0, -1.2, -1}
## 291 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2.3}
## 292 {0, 0, 0, 0, 0}
## 293 {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, 1.1}
## 294 {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, 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}
## 296 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.11, 0, 0, 0}
## 297 {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, 0, 0, -2.5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 298 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.85, 0, -2.1, 0, -4.2, 0}
## 299 {0, 0, 0, 0, -0.9, 0, 0, 0, 0, -2.8, -1.4, 0, 0, 0, 0, 0, 0, -1.393, 0, 0, 0, 0, 0, 0.9, 0, 0, 0, 0, 0, -1.67135, 0, 0, 0, 0, 0, 0}
## 300 {0, 0, 0, 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, -0.962, 0, 0, 0, 0, 0, 0, 0, 0, 1.9, 0}
## 301 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.184, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 302 {0, 0.9, 0, 0, 0, 1.8, 0, 0, 0, 0, 0}
## 303 {0, 0, 0, 0, 0, 0, 0, 0, 0.3, 0, 0, 0, 0, 0, 0, 0}
## 304 {0, 0, 0, 3.2, 0, 0, 0, 0, 0}
## 305 {0, 0, 0, 0}
## 306 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 307 {0, 0, 0, 2.093, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.3, 0, 0}
## 308 {0, 0, 2.393, 0, 0, 0, 0, 0, 0, 0, 0}
## 309 {0, 0, 0, 0, 0, 0, 0, 0, 0}
## 310 {0, 0, 0, 0, 0, 0, 0, 0, 0}
## 311 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 312 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -0.518, 0, 0, 0, 0, 0, 0, 0, -0.518, 0, 0, 0}
## 313 {0, 0, 0, 0, 0, 0, 0, 2.2, 0, 0, 0}
## 314 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.3, 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, 0, 0, 0, 0, 0, 0, 0, 0}
## 315 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.893, 0, 0, 0, 0, 0, 0, 0, 0}
## 316 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3.2, 0, 0, 0, 0, 0, 0, 0, 0, 3.2, 0, 0, 0, 0, 0, 0, 0, 0, 3.2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 317 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2.85}
## 318 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 319 {0, -1.7, 0, 0, 1.9, 0, -1.2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.2, 0, 0, 0, 0, 0, 0, 0, -1.6, 0}
## 320 {0, 0, 0.3, 0, 0, -3, 0, 0.3, 0, 0, 0, -1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 321 {0, 0, 0, 0, 1.7, 0, 0, 0, 0, 0}
## 322 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -2.4, 1.5, 0}
## 323 {0, 0, 0, 0, 0, 0, 0}
## 324 {0, 0, 0, 0, 0, 0, 0}
## 325 {0, 0, 0, 0, 0, -2.1, 0, 0, 0, 0, 0, 0, 0}
## 326 {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}
## 327 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 328 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.8, 0, 0, 0, 0, 0}
## 329 {0, 0, 0, -2.5, 0, 0, 0, 0, 0, 0, 1.3, 0}
## 330 {0, 0, 0, 1.5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.628, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 331 {0, -1.8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.1}
## 332 {0, 0, 0, 0, 0, 0, 0, 0, -1.7, 0, 0, 0, 0, 0}
## 333 {0, 0, 0, 0, 0, 0, 0, 0, 0}
## 334 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 335 {0, 0, 0, 0, 0, 0, 0, 0, 0.9, 0, 0, 0, 0, 0, 0, 0, 0, -1.3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.8, 0}
## 336 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 337 {0, 1.7, 0, 0, 0, 0, 0, 0, 0, 0, 0, -2, 0, 0, 0, 0, 0, 1.8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 338 {0, 0, -2.6, 0, 0, 0, 0, 1.6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.776, 0, 0, 0}
## 339 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 340 {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, -1.7, 0, 0, 0, 0, 0.3, 0}
## 341 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -2.6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 342 {0, 0, 0, 0, 0, -2.3, 0, 0, 0}
## 343 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 344 {0, 0, 0, 0}
## 345 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2.25, 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}
## 347 {0, -1.2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.3, 0, 0, 0, 0, 0, -1.5}
## 348 {0, 0, 0, 0, 0, 0, 0, 0}
## 349 {0, -1.2, 0, 0, 1.8, 0, 0, 0}
## 350 {0, 0, -1.133, -1.833, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 351 {0, 0, 0, 0, 0, 2.2, 0, 0, -1.628, 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, -1.406, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 352 {0, 0, 0, 1.5, 0, 0, 0, 0}
## 353 {0, 0, 0, 0, 0, 0, 0, -4.05, 0, 0, 0, 0, 0, 0, 0, 0, 0, -2.6895}
## 354 {0, 1.1, 0, 0, 3.2, 0, 0}
## 355 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 356 {-1.4, 0, 0, 0, 0, 0, 0}
## 357 {0, 0, 0, 0, 0, 0, 0, 0, 0, -1.9, 0, 0, 0, 0, 0, 0, 0, 1.8, 0, 0, 0, 0, 0, 0}
## 358 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.3, 0, -3.2, 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, 0, 0, 0, 0, 0, 0, 0}
## 359 {0, 1.9, 0, 0, 0, 0, 0, 0, 0, 0, 1.5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 360 {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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 361 {0, 0, 0, 0}
## 362 {0, 0, -3.2, 0, 0, 0, 0, 0, 1.5, 0, 0, 1.5, 0, 0, 0, 0, 0, 0, -2.5}
## 363 {0, 0, 1.8, 0, 0, 0, 0, 0, 3.2, 0, 0, 0, 0, 0, 0}
## 364 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.2, 0, 0, -0.4, 0, 0, -1.11, 0}
## 365 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 366 {0, 0, 0, 0, 0, 0, 0, 0, 0, -1.823138, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.8, 0, 0, 0, 0, 0, 0, 0, -3.5, 0, 0, 0, -3.4, 0, 0, 0, -2.1, 0, -2.393}
## 367 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.8, 0, -0.7, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 368 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0}
## 369 {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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.35, 0, 0, 0, 0}
## 370 {0, 0, 0, 0, 0, 0, 0}
## 371 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 372 {0, 0, 0, 0, 0, 0, 0, -2.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.9, 0, 0, 0, 0, 0}
## 373 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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, 1, 0, 0, 0, 0, 0, 1, 0, 0}
## 374 {0, -1.2, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 375 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 376 {0, 0, 0, 0, 0, 0, 0, -1.7, 0, 0, 0, 0, 0, 0, 2.8, 0, 0, 0}
## 377 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 378 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2.1, 0, 0, 0}
## 379 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 380 {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}
## 381 {0, 0, 0, -1.6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2.1, 0, 0, 0, 0, 0, 0, 0, 0, 1.85, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.9, 0, 0, 0, 0, 0, 0, 0}
## 382 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 383 {0, 0, 0, 0, 0, 0}
## 384 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 385 {0, 0, -2.3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 386 {0, 0, 0, 0, 0, 0, 0, 0, 2.7, 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}
## 387 {3.083, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.7, 0, 0, 0, 0, 0, 0, 0}
## 388 {0, 0, 0.8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 389 {0, 0, 0, -0.555, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.518, 0, 0, 0, 0, 0, 0, 0, 0, 0.65, 0, 0, 0, 0, 0, -1.2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.7, -0.9, 0, 0, 0, 0, 0, 0, 0, 0}
## 390 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 391 {0, 0, 0, 0, 0, -1.406, 0, 0, 0, 0, 0, 0, 1.1, 0, 0, 0, 0, 0, 0}
## 392 {0, 0, 0, 0, 0, 0, 0, 0, 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}
## 393 {0, 0, 0, 0, 0, 0}
## 394 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.5, 0, 0, -1.8, 0, 0, 0, 0, 0, 0, 0, -2.233, 0, -2.6, 0, 0, 0, -1.7}
## 395 {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, 1.9, 0, 0, 0}
## 397 {0, 0, 0}
## 398 {0, 0, 0, 0}
## 399 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 400 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 401 {0, 0, 0, 0}
## 402 {0, -2.1, 0, 0, 0, 0, 0, 0}
## 403 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, -2.3, 0}
## 404 {0, 0, 0, 0, 0, 0, 0}
## 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, -1.4, 1.5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 406 {0, 0, 0, 0, 0, 0, -2, 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, 1.2, 0, 0, 0, 0}
## 407 {0, 0, 0, 0, 1.3, 0, 0, 0, -1.4, 0, 0, 0, -1.258, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 408 {0, 0, 0, -0.5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -2.7}
## 409 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 410 {0, 0, 1.3, 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, 2.22, 0, 0}
## 411 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 412 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.4, 0, 0, 0, 0, 0, 0, 0, 0}
## 413 {0, 0, 0, 0, 1.3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 414 {0, 0, 0, 0, 0, -1.2, 0, 0, 0}
## 415 {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}
## 416 {0, 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, -0.8, 0}
## 417 {0, 0, 0, 0, 1.7, 1.4, 0, 0, 0, 0, 0, 0, 0, 1.7, 0, 0, 0, 0, 0, 0}
## 418 {0, 0, 0, 0, 0, 0, 0}
## 419 {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, -2.2, 0, 0}
## 420 {0, 0, 0, -1.35, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 421 {0, 0, -0.85, 0.9, 0, 0, 0.85, 0, 0, 0, 0, 0, 0, 0, 0, 0.8, 0, 0, 0, 0, 0, -2.25, 0, 0, 0, 0, 0, 0, -4.05, 0, 0, -3.6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -3.45, 0, 0}
## 422 {0, 0, 0, 0, 0, 0, 0, 0, 0}
## 423 {0, 0, 0, 0, 0, 0, 0, 0, 0}
## 424 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -0.7, 0, 0, 0, 0}
## 425 {0, 0, 0, 0, 0, 0, 0, 0, 0.85, 0, 1.15, 0, 0, 0, 0, 0, 0, 0, -0.3, 0, 0, 0, 0, 0, 0, 0, 0, -0.555, -0.555, 0, 0, 0, 0, 0, -3, 0, 0, 0, 0, 0, 0}
## 426 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 427 {0, 0, 0, 0, 0, 0, 0, -0.2, 0, 0, 0, 0}
## 428 {0, 0, -1.2, 0, 0, 0, -2.1, 0, 0, 0, 0, -1.2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 429 {0, 0, 0, 0, 0, 0, 0, 0, 0, -1.4, 0, 0}
## 430 {0, 1.2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 431 {0, 0, 1.4, 0, 0, 0, 1.9, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 432 {0, 0, 0, 0, 0, 0, -2.9, 0, 0, 1.7, 0, 0}
## 433 {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, -1.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.3, 0, 0, 0, 0, 0}
## 434 {0, 0, 0, 0, 0, 0}
## 435 {0, 1.5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 436 {0, 0, 0, 0, 0, 0, 0, -0.6, 0, 0, 0, 0, 0, 0, 0, -2.25, 0, 0, 0, 0, 0, 0, 0, 0, -3.15, 0, 0, -4.35, 0, -2.1, 0, -1.65, 0, 0, 0, 0, 0, 0, 0, 0, -5.34555, 0, 0, 0}
## 437 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 438 {0, 0, 1.3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 439 {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}
## 440 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -2.1, 0, 0, 0, 0, 0, 2.9, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 441 {0, 0, 0, 0, 0, 0, 0, 0, -1.2, 0, 0, 0, 1.7, 0, 0, 0, 0, 0, 2.7, -2.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2.3, 0, 0, 0, -0.4, 0, 0, 0, 0, 0, 2.8, 0, 0, 0}
## 442 {0, 0, -1.5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 443 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.233, 0, 0, 0, 0, 0, 0, 1.233, 0, 0, 0, 0, 0, 1.7, 0, 0, 1.233, 0, 0, 0, 0, 1.3, 0, 0, 0, 0, 0, 0, 0}
## 444 {0, 0, 0, 1.7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.2, -0.5, 0, 0, 0}
## 445 {0, 0, 0, 0, 0, 0, 0, 0, 0, 2.35}
## 446 {0, 0, 0, 0}
## 447 {0, 0, 2.3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.1, 0}
## 448 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 449 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 450 {0, 0, 3.2, 0, 0, 2.35, 0}
## 451 {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}
## 452 {0, 0, 0, 0, 0, -2.1, 0, 0, 0, 0, 0, 0, 0, 0, 2.35}
## 453 {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}
## 454 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 455 {0, 0, -2.1, 0, 0, 0, 0, 0, 0, 1.3, 0, 0, 0, 0, 0, 0, 0, 0, 1.1}
## 456 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 457 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 458 {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}
## 459 {1.9, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -2.493, 0, 0, 0}
## 460 {0, 0, -1.2, 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, 0, 0, 0, 0, -1.6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.8, 0, 2.2}
## 461 {0, 0, 0, 0, 0, -2.5, 0, 0, 0, 0, 0, 0, 0, 0, 0.3, 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}
## 462 {0, 0, 0, 0, 0, 0, 0, 1.7, 0, 0, 0, 0, 0, 0, 0, 0, 0, -2.9, 0, 0, 0, 0, -2.9, 0, 0, 0, 0, -2.9, 0, 0, 0, 0, 0, 0, 0, 0, 0, -0.962, 0, 0, 0, 0, 0, 0, 0, 0}
## 463 {0, 0, 0, 0}
## 464 {0, 0, 0, 0, 0, 2.7, 0, 0, 1.5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2.6, 1.7, 0, 0, 0, 0, 0, 2.7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 465 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 466 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 467 {0, 2.3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2.3, 0, 0, 0, 0, 0}
## 468 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 469 {0, 0, 0, 0, 0, 0, 0, 0}
## 470 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 471 {0, 0, 0, 0}
## 472 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 473 {0, 0, 0, -1.3, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.4, -1.7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.7, 0, 0, 0, 0, -2.3, 0, 0, 0, -1.7}
## 474 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 475 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.702, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.9, 0, 0, 0, 2.3, 0, 0, 0, 0, 0}
## 476 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 477 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 478 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 479 {0, 0, 0, 0, 0, 0, 0, 0, 0.1, 0, 0, 0, 0, 0}
## 480 {0, 0, 0, 0, 0, 1.5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 481 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 482 {0, 0, 0, 0, 0, 0, 0}
## 483 {0, 0, 0, 0}
## 484 {0, 0, 0, 0, 0, 0, 0}
## 485 {0, 0, 0, 0, -1.3, 0, 0, 0, 0, 0, 0, -1.5637, 0, 0, 0, 1.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1, 0, 0}
## 486 {0, 0, 1.5, 0, 0, 0, 0, 0, 0, 0, 0, 0, -2.793, 0, 0, 0, 0, 1.1, 0, 0}
## 487 {0, 0, 0, 0, 0, 0, 0, 0, 0, 1.9, 0, 0, 0}
## 488 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 489 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3.1, 0, 0, 0, 0}
## 490 {0, 0, 0, 0, 0, 0, -2.3, 0, 0, 0, 0, -2.4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.8, 0, 1.1, 2.1, 0, 0, 0, 0, 2.2}
## 491 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -2.2, 0, 0, 0}
## 492 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2.3, 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, 0.9}
## 493 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 494 {0, 2.5, 2.3, 0, 0, 0, 0, 0, -1.007, 0, 0, 0, 0, 0, 0, 0, 1.8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2.3, 0, 0, 0, 0, 0, 0, 0, 0, 2.1, 1.4, 0}
## 495 {0, 0, 1.1, 0, 0, 0, -1.5, 0, 0, 0, 0, 0, -1.2, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 496 {0, 0, 0, 0, 0, 0, 0, 0, -3.1395, 0, 0, 0, 0, 0, 0, 0, 2.55, 2.85, 2.25, 0, 1.5, 1.65, 0.15, 3.3, 3.9, 0, 0, 0, 0, 0, 0, 1.5, 0, 0, 2.4, 2.25, 3.675, 0, 3.45, 4.8}
## 497 {0, 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}
## 498 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2.233, 0, 0, -3.533, 0, 0, 0}
## 499 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -2.1, 0, 0, 0, 0, 0}
## 500 {-1.2, 0, 0, -2.5, 0, 0, 0, 0, 0, -2.3, 0, 0, 0, 0, 0}
## compound pos neu neg but_count
## 1 0.000 0.000 1.000 0.000 0
## 2 -0.511 0.000 0.809 0.191 0
## 3 0.765 0.127 0.842 0.031 0
## 4 -0.482 0.064 0.810 0.126 0
## 5 -0.318 0.000 0.926 0.074 1
## 6 0.000 0.000 1.000 0.000 0
## 7 0.000 0.000 1.000 0.000 0
## 8 0.052 0.107 0.793 0.100 0
## 9 0.000 0.000 1.000 0.000 0
## 10 -0.250 0.000 0.938 0.062 0
## 11 0.655 0.198 0.711 0.091 0
## 12 -0.445 0.080 0.804 0.117 0
## 13 -0.718 0.000 0.816 0.184 0
## 14 0.000 0.000 1.000 0.000 0
## 15 0.420 0.259 0.741 0.000 0
## 16 -0.599 0.112 0.675 0.213 0
## 17 -0.566 0.000 0.893 0.107 0
## 18 -0.340 0.000 0.854 0.146 0
## 19 -0.557 0.000 0.690 0.310 0
## 20 0.000 0.000 1.000 0.000 0
## 21 -0.248 0.094 0.773 0.134 0
## 22 -0.557 0.000 0.816 0.184 0
## 23 0.780 0.215 0.693 0.092 0
## 24 0.200 0.105 0.811 0.084 0
## 25 0.000 0.000 1.000 0.000 0
## 26 -0.710 0.000 0.862 0.138 0
## 27 0.000 0.000 1.000 0.000 0
## 28 -0.583 0.000 0.859 0.141 0
## 29 0.000 0.000 1.000 0.000 0
## 30 0.504 0.214 0.786 0.000 0
## 31 -0.296 0.122 0.691 0.186 0
## 32 0.791 0.210 0.712 0.079 0
## 33 -0.468 0.000 0.877 0.123 2
## 34 -0.026 0.052 0.893 0.055 0
## 35 0.542 0.137 0.803 0.061 0
## 36 -0.494 0.088 0.780 0.132 0
## 37 0.000 0.000 1.000 0.000 0
## 38 -0.477 0.000 0.866 0.134 0
## 39 -0.822 0.000 0.830 0.170 1
## 40 0.000 0.000 1.000 0.000 0
## 41 -0.442 0.034 0.902 0.064 2
## 42 0.000 0.000 1.000 0.000 0
## 43 0.872 0.239 0.705 0.056 0
## 44 0.000 0.000 1.000 0.000 0
## 45 0.000 0.000 1.000 0.000 0
## 46 0.000 0.000 1.000 0.000 0
## 47 0.212 0.195 0.638 0.167 0
## 48 0.151 0.071 0.873 0.056 0
## 49 0.000 0.000 1.000 0.000 0
## 50 0.577 0.151 0.849 0.000 0
## 51 -0.542 0.000 0.741 0.259 0
## 52 0.340 0.074 0.926 0.000 0
## 53 0.822 0.158 0.842 0.000 0
## 54 -0.103 0.127 0.733 0.140 0
## 55 0.000 0.000 1.000 0.000 0
## 56 0.000 0.000 1.000 0.000 0
## 57 -0.361 0.000 0.762 0.238 0
## 58 -0.361 0.000 0.615 0.385 0
## 59 0.225 0.075 0.847 0.078 0
## 60 0.000 0.000 1.000 0.000 0
## 61 0.000 0.000 1.000 0.000 0
## 62 0.361 0.135 0.865 0.000 0
## 63 -0.735 0.000 0.618 0.382 0
## 64 0.379 0.062 0.938 0.000 0
## 65 -0.765 0.000 0.875 0.125 0
## 66 0.000 0.082 0.836 0.082 0
## 67 0.000 0.000 1.000 0.000 0
## 68 0.000 0.000 1.000 0.000 0
## 69 0.527 0.132 0.868 0.000 0
## 70 0.318 0.277 0.723 0.000 0
## 71 -0.927 0.000 0.682 0.318 1
## 72 0.153 0.064 0.936 0.000 0
## 73 0.906 0.270 0.730 0.000 0
## 74 0.000 0.000 1.000 0.000 0
## 75 0.000 0.000 1.000 0.000 0
## 76 -0.887 0.000 0.779 0.221 0
## 77 -0.485 0.000 0.908 0.092 1
## 78 0.437 0.081 0.888 0.031 0
## 79 0.238 0.037 0.963 0.000 2
## 80 0.494 0.167 0.833 0.000 0
## 81 -0.629 0.000 0.708 0.292 0
## 82 0.881 0.275 0.725 0.000 0
## 83 0.800 0.235 0.662 0.103 0
## 84 0.380 0.105 0.895 0.000 0
## 85 -0.802 0.086 0.721 0.194 0
## 86 -0.202 0.091 0.787 0.122 0
## 87 0.439 0.053 0.947 0.000 0
## 88 0.818 0.195 0.805 0.000 0
## 89 0.000 0.000 1.000 0.000 0
## 90 0.445 0.226 0.774 0.000 0
## 91 0.000 0.000 1.000 0.000 0
## 92 0.564 0.149 0.762 0.090 1
## 93 0.000 0.000 1.000 0.000 0
## 94 -0.389 0.043 0.860 0.097 0
## 95 0.000 0.000 1.000 0.000 1
## 96 0.855 0.164 0.836 0.000 0
## 97 -0.477 0.000 0.659 0.341 0
## 98 0.519 0.295 0.705 0.000 0
## 99 0.000 0.000 1.000 0.000 0
## 100 -0.542 0.072 0.754 0.174 1
## 101 0.361 0.143 0.857 0.000 0
## 102 0.000 0.000 1.000 0.000 0
## 103 0.273 0.075 0.925 0.000 0
## 104 0.665 0.205 0.739 0.055 1
## 105 0.361 0.106 0.894 0.000 0
## 106 -0.778 0.000 0.585 0.415 0
## 107 0.148 0.072 0.847 0.080 1
## 108 0.458 0.071 0.929 0.000 0
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## 500 -0.840 0.000 0.571 0.429 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?
It seems like some tweets are not accurately identified. For example, the third tweet has several positive words (love) but the seniment is 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] 0.002678Overall, 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
## 1 163 161 176 0.9877301Not 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:
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: 8125 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.ngss_sample## # A tibble: 500 × 8
## text created_at author_id id conversation_id source
## <chr> <dttm> <dbl> <dbl> <dbl> <chr>
## 1 "@BMSsciencete… 2021-01-15 14:01:52 1.85e 8 1.35e18 1.35e18 Twitte…
## 2 "@jotregenza @… 2021-05-13 17:46:17 2.35e 9 1.39e18 1.39e18 Twitte…
## 3 "Absolutely lo… 2021-05-19 15:27:16 1.39e18 1.40e18 1.40e18 Instag…
## 4 "Draw an icebe… 2021-03-12 21:23:22 2.07e 7 1.37e18 1.37e18 Twitte…
## 5 "@sknelson09 @… 2021-01-19 20:19:20 8.37e17 1.35e18 1.35e18 Twitte…
## 6 "@philiplbell … 2021-02-03 16:18:09 2.42e 8 1.36e18 1.36e18 Twitte…
## 7 "Highly recomm… 2021-03-28 00:08:38 7.85e17 1.38e18 1.38e18 Twitte…
## 8 "We recognize … 2021-03-21 01:30:04 1.85e 8 1.37e18 1.37e18 social…
## 9 "Hot take- my … 2021-04-06 15:09:40 7.03e17 1.38e18 1.38e18 Twitte…
## 10 "New Year! New… 2021-01-06 16:59:32 1.05e18 1.35e18 1.35e18 Twitte…
## # … with 490 more rows, and 2 more variables: possibly_sensitive <lgl>,
## # in_reply_to_user_id <dbl>vader_ngss <- vader_df(ngss_sample$text)
mean(vader_ngss$compound)## [1] 0.388962vader_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
## 1 343 39 118 0.1137026How do our results compare to the CSSS sample of tweets?
- The NGSS sample of tweets is more positive than the CSSS sample.
How do these result compare to those found by Rosenberg et al. (2021)?
- From the Rosenberg et al (2021) article stated “The sentiment ratio (the ratio of negative to positive posts) of 0.11 for posts about the NGSS.” The sentiment ratio for NGSS sample tweets from this case study was very similar (0.10)
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?
- Excluding the words/phrases that make up CCSS and NGSS, for CCSS, “math,” “education,” “kids and school” were some of the most frequent; for NGSS, “students,” “science,” and learning" were some of the most frequent.
How does sentiment for NGSS compare to sentiment for CCSS?
- NGSS was more positive (354) than CCSS in addition to having a more positive mean value. CCSS in comparison had less positive tweets from the sample and the ratio was less positive.
Finally, add 1-2 sentences in response to the following prompts:
One important thing I took away from this case study about text mining :
- I enjoyed seeing how this applied to tweets and thinking about other applications! I really enjoyed making visusals like the word clouds.
One thing about text mining I want to learn more about:
- I’d like to see how this could be applied to things like politics, social justice movements/reform, etc. It’d be interesting to see what we could find out about other topics that are still public facing.
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:
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")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: 37,539 × 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 ngss_tweeps ngsschat 96
## 8 science ngss 94
## 9 bmsscienceteach ngss_tweeps 92
## 10 approved approach 89
## # … with 37,529 more rowsYour Turn ⤵
Use the code chunk below to tidy and count our bigrams for the CCSS tweets:
What additional insight, if any, did looking at bigrams bring to out analysis?
- YOUR RESPONSE HERE
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 <- "Text Mining in Education"
api_key <- "afYS4vbIlPAj096E60c4W1fiK"
api_secret_key <- "bI91kqnqFoNCrZFbsjAWHD4gJ91LQAhdCJXCj3yscfuULtNkuu"
access_token <- "9551451262-wK2EmA942kxZYIwa5LMKZoQA4Xc2uyIiEwu2YXL"
access_token_secret <- "9vpiSGKg1fIPQtxc5d5ESiFlZQpfbknEN1f1m2xe5byw7"

## 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}

```

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

    -   

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

    -   

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

    -   

#### 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, 
                                   ##ainclude_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}

```

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

    -   

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

    -   

**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.

#### 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.

### 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?

-   I think Twitter users are more negative about CCSS than NGSS.

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 seem to include things not as relative to common core (politics, etc.). It also seems like there are more hashtags being used in the NGSS data set.

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

    -   I don't have any questions now, but I'm curious to see if my opinion changes over time.

### 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(author_id, conversation_id, created_at, id, text)

```

#### Add & Relocate Columns

Finally, since we are interested in comparing the sentiment of NGSS tweets with CSSS 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 "ngss":

```{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_1 <- ss_tweets %>%
  unnest_tokens(output = word, input = text, token = "tweets")

ss_tokens_1
```

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

    -   35233 observations

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

    -   874,238 observations.

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_1 %>%
  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_1,
                         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?

    -   73,596 (as indicated by the number of rows)

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

    -   11,788 times

#### 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 to 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}
ngss_top_tokens <- ss_tidy_tweets %>%
  filter(standards == "ngss") %>%
  count(word, sort = TRUE) %>%
  top_n(50)

wordcloud2(ngss_top_tokens)

```

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).

    -   From Chapter 2, "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".

2.  Why should we be cautious when using and interpreting them?

    -   There are some limitations; sentiment lexicons could be less accurate depending on what is being measured (Jane Austen novels); they also do not contain every English word and may not be intuitive enough to understand things like sarcasm and how that effects negative/positive ranking. As well, the size could impact the score.

#### 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?

It seems like some tweets are not accurately identified. For example, the third tweet has several positive words (love) but the seniment is 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}
ngss_sample <- read_csv(here("unit-3", "data", "ngss-tweets.csv")) %>%
  sample_n(500)

ngss_sample

vader_ngss <- vader_df(ngss_sample$text)

mean(vader_ngss$compound)

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?

    -   The NGSS sample of tweets is more positive than the CSSS sample.

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

    -   From the Rosenberg et al (2021) article stated "The sentiment ratio (the ratio of negative to positive posts) of 0.11 for posts about the NGSS". The sentiment ratio for NGSS sample tweets from this case study was very similar (0.10)

## 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?

    -   Excluding the words/phrases that make up CCSS and NGSS, for CCSS, "math", "education", "kids and school" were some of the most frequent; for NGSS, "students", "science", and learning" were some of the most frequent.

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

    -   NGSS was more positive (354) than CCSS in addition to having a more positive mean value. CCSS in comparison had less positive tweets from the sample and the ratio was less positive.

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 :

    -   I enjoyed seeing how this applied to tweets and thinking about other applications! I really enjoyed making visusals like the word clouds.

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

    -   I'd like to see how this could be applied to things like politics, social justice movements/reform, etc. It'd be interesting to see what we could find out about other topics that are still public facing.

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}

```

### 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")
```

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}


```

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

-   YOUR RESPONSE HERE

### References

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