Guiding questions for this walkthrough are:

  1. What is the public sentiment expressed toward the NGSS?
  2. How does sentiment for NGSS compare to sentiment for CCSS?

###1. Set Up a new project, open up a new R script, and load the following packages that we’ll be needing for this walkthrough:

library(dplyr)
library(readr)
library(tidyr)
library(rtweet)
library(writexl)
library(readxl)
library(tidytext)
library(textdata)
library(ggplot2)
library(textdata)
library(scales)

###2. WRANGLE

  1. Import Data. In this section, we introduce the rtweet package and some key functions to search for tweets or users of interest.
  2. Tidy Tweets. We revisit the tidytext package to both “tidy” and tokenize our tweets in order to create our data frame for analysis.
  3. Get Sentiments. We conclude our data wrangling by introducing sentiment lexicons and the inner_join() function for appending sentiment values to our data frame.

2a. Import Tweets

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

Create Dictionaries for ngss and ccss

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

Write to Excel

# write_xlsx(ngss_tweets, "data/ngss_tweets.xlsx")
# write_xlsx(ccss_tweets, "data/csss_tweets.xlsx")

2b. Tidy Text

Functions Used

dplyr functions

  • select() picks variables based on their names.
  • slice() lets you select, remove, and duplicate rows.
  • rename() changes the names of individual variables using new_name = old_name syntax
  • filter() picks cases, or rows, based on their values in a specified column.

tidytext functions

  • unnest_tokens() splits a column into tokens
  • anti_join() returns all rows from x without a match in y.

ATTENTION: For those of you who do not have Twitter Developer accounts, you will need to read in the Excel files share in our github.

We’ll use the readxl package highlighted in Lab 1 and the read_xlsx() function to read in the data stored in the data folder of our R project:

ngss_tweets <- read_xlsx("data/ngss_tweets.xlsx")
ccss_tweets <- read_xlsx("data/csss_tweets.xlsx")

First, use the filter function to subset rows containing only tweets in the language Second, select the following columns from our new ngss_text data frame: 1. screen_name of the user who created the tweet 2. created_at timestamp for examining changes in sentiment over time 3. text containing the tweet which is our primary data source of interestt Third, use the mutate() function to create a new variable called standards to label each tweets as “ngss” Fourth, use the relocate() function to move the standards column to the first position

ngss_text <-
  ngss_tweets %>%
  filter(lang == "en") %>%
  select(screen_name, created_at, text) %>%
  mutate(standards = "ngss") %>%
  relocate(standards)

Create an new ccss_text data frame for our ccss_tweets Common Core tweets by modifying code above.

Combine Data Frames

tweets <- bind_rows(ngss_text, ccss_text)

Take a quick look at both the head() and the tail() of this new tweets data frame to make sure it contains both “ngss” and “ccss” standards:

head(tweets)
## # A tibble: 6 × 4
##   standards screen_name  created_at          text                               
##   <chr>     <chr>        <dttm>              <chr>                              
## 1 ngss      loyr2662     2021-02-27 17:33:27 "Switching gears for a bit for the…
## 2 ngss      loyr2662     2021-02-20 20:02:37 "Was just introduced to the Engine…
## 3 ngss      Furlow_teach 2021-02-27 17:03:23 "@IBchemmilam @chemmastercorey I’m…
## 4 ngss      Furlow_teach 2021-02-27 14:41:01 "@IBchemmilam @chemmastercorey How…
## 5 ngss      TdiShelton   2021-02-27 14:17:34 "I am so honored and appreciative …
## 6 ngss      TdiShelton   2021-02-27 15:49:17 "Thank you @brian_womack I loved c…
tail(tweets)
## # A tibble: 6 × 4
##   standards screen_name   created_at          text                              
##   <chr>     <chr>         <dttm>              <chr>                             
## 1 ccss      JosiePaul8807 2021-02-20 00:34:53 "@SenatorHick You realize science…
## 2 ccss      ctwittnc      2021-02-19 23:44:18 "@winningatmylife I’ll bet none o…
## 3 ccss      the_rbeagle   2021-02-19 23:27:06 "@dmarush @electronlove @Montgome…
## 4 ccss      silea         2021-02-19 23:11:21 "@LizerReal I don’t think that’s …
## 5 ccss      JodyCoyote12  2021-02-19 22:58:25 "@CarlaRK3 @NedLamont Fully fund …
## 6 ccss      Ryan_Hawes    2021-02-19 22:41:01 "I just got an \"explainer\" on h…

Tokenize Text

tweet_tokens <- 
  tweets %>%
  unnest_tokens(output = word, 
                input = text)

Remove Stop Words; Custom Stop Words

tidy_tweets <-
  tweet_tokens %>%
  anti_join(stop_words, by = "word") %>%
  filter(!word == "amp") %>%
  filter(!word == "t3ic")

2c. Add Sentiment Values

Get Sentiments

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.

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
## # ℹ 2,467 more rows
bing <- 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 
## # ℹ 6,776 more rows
nrc <- get_sentiments("nrc")

nrc
## # A tibble: 13,872 × 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     
## # ℹ 13,862 more rows

And 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 
## # ℹ 4,140 more rows

Let’s use inner_join() to combine our two tidy_tweets and afinn data frames, keeping only rows with matching data in the word column:

sentiment_afinn <- inner_join(tidy_tweets, afinn, by = "word")

sentiment_afinn
## # A tibble: 1,540 × 5
##    standards screen_name  created_at          word         value
##    <chr>     <chr>        <dttm>              <chr>        <dbl>
##  1 ngss      loyr2662     2021-02-27 17:33:27 win              4
##  2 ngss      Furlow_teach 2021-02-27 17:03:23 love             3
##  3 ngss      Furlow_teach 2021-02-27 17:03:23 sweet            2
##  4 ngss      Furlow_teach 2021-02-27 17:03:23 significance     1
##  5 ngss      TdiShelton   2021-02-27 14:17:34 honored          2
##  6 ngss      TdiShelton   2021-02-27 14:17:34 opportunity      2
##  7 ngss      TdiShelton   2021-02-27 14:17:34 wonderful        4
##  8 ngss      TdiShelton   2021-02-27 14:17:34 powerful         2
##  9 ngss      TdiShelton   2021-02-27 15:49:17 loved            3
## 10 ngss      TdiShelton   2021-02-27 16:51:32 share            1
## # ℹ 1,530 more rows

Notice that each word in your sentiment_afinn data frame now contains a value ranging from -5 (very negative) to 5 (very positive).

sentiment_bing <- inner_join(tidy_tweets, bing, by = "word")

sentiment_bing
## # A tibble: 1,668 × 5
##    standards screen_name  created_at          word         sentiment
##    <chr>     <chr>        <dttm>              <chr>        <chr>    
##  1 ngss      loyr2662     2021-02-27 17:33:27 win          positive 
##  2 ngss      Furlow_teach 2021-02-27 17:03:23 love         positive 
##  3 ngss      Furlow_teach 2021-02-27 17:03:23 helped       positive 
##  4 ngss      Furlow_teach 2021-02-27 17:03:23 sweet        positive 
##  5 ngss      Furlow_teach 2021-02-27 17:03:23 tough        positive 
##  6 ngss      TdiShelton   2021-02-27 14:17:34 honored      positive 
##  7 ngss      TdiShelton   2021-02-27 14:17:34 appreciative positive 
##  8 ngss      TdiShelton   2021-02-27 14:17:34 wonderful    positive 
##  9 ngss      TdiShelton   2021-02-27 14:17:34 powerful     positive 
## 10 ngss      TdiShelton   2021-02-27 15:49:17 loved        positive 
## # ℹ 1,658 more rows
## Warning in inner_join(tidy_tweets, nrc, by = "word"): Detected an unexpected many-to-many relationship between `x` and `y`.
## ℹ Row 24 of `x` matches multiple rows in `y`.
## ℹ Row 7509 of `y` matches multiple rows in `x`.
## ℹ If a many-to-many relationship is expected, set `relationship =
##   "many-to-many"` to silence this warning.
## Warning in inner_join(tidy_tweets, loughran, by = "word"): Detected an unexpected many-to-many relationship between `x` and `y`.
## ℹ Row 2297 of `x` matches multiple rows in `y`.
## ℹ Row 2589 of `y` matches multiple rows in `x`.
## ℹ If a many-to-many relationship is expected, set `relationship =
##   "many-to-many"` to silence this warning.

3a. Time Series

Before we dig into sentiment, let’s use the handy ts_plot function built into rtweet to take a very quick look at how far back our tidied tweets data set goes:

ts_plot(tweets, by = "days")

✅ Comprehension Check
  1. Use ts_plot with the group_by function to compare the number of tweets over time by Next Gen and Common Core standards
  2. Which set of standards is Twitter users talking about the most?

Hint: use the ?ts_plot help function to check the examples to see how this can be done.

Your line graph should look something like this:

3b. Sentiment Summaries

Revisit the following dplyr functions:

  • count() lets you quickly count the unique values of one or more variables

  • group_by() takes a data frame and one or more variables to group by

  • summarise() creates a numerical summary of data using arguments like mean() and median()

  • mutate() adds new variables and preserves existing ones

And introduce one new function:

  • spread()

Sentiment Counts

summary_bing <- sentiment_bing %>% 
  group_by(standards) %>% 
  count(sentiment) 

summary_bing
## # A tibble: 4 × 3
## # Groups:   standards [2]
##   standards sentiment     n
##   <chr>     <chr>     <int>
## 1 ccss      negative    926
## 2 ccss      positive    446
## 3 ngss      negative     66
## 4 ngss      positive    230

Compute Sentiment Value

summary_bing <- sentiment_bing %>% 
  group_by(standards) %>% 
  count(sentiment, sort = TRUE) %>% 
  spread(sentiment, n) 

summary_bing
## # A tibble: 2 × 3
## # Groups:   standards [2]
##   standards negative positive
##   <chr>        <int>    <int>
## 1 ccss           926      446
## 2 ngss            66      230

Finally, we’ll use the mutate function to create two new variables: sentiment and lexicon so we have a single sentiment score and the lexicon from which it was derived:

summary_bing <- sentiment_bing %>% 
  group_by(standards) %>% 
  count(sentiment, sort = TRUE) %>% 
  spread(sentiment, n) %>%
  mutate(sentiment = positive - negative) %>%
  mutate(lexicon = "bing") %>%
  relocate(lexicon)

summary_bing
## # A tibble: 2 × 5
## # Groups:   standards [2]
##   lexicon standards negative positive sentiment
##   <chr>   <chr>        <int>    <int>     <int>
## 1 bing    ccss           926      446      -480
## 2 bing    ngss            66      230       164

There we go, now we can see that CCSS scores negative, while NGSS is overall positive.

Let’s calculate a quick score for using the afinn lexicon now. Remember that AFINN provides a value from -5 to 5 for each:

head(sentiment_afinn)
## # A tibble: 6 × 5
##   standards screen_name  created_at          word         value
##   <chr>     <chr>        <dttm>              <chr>        <dbl>
## 1 ngss      loyr2662     2021-02-27 17:33:27 win              4
## 2 ngss      Furlow_teach 2021-02-27 17:03:23 love             3
## 3 ngss      Furlow_teach 2021-02-27 17:03:23 sweet            2
## 4 ngss      Furlow_teach 2021-02-27 17:03:23 significance     1
## 5 ngss      TdiShelton   2021-02-27 14:17:34 honored          2
## 6 ngss      TdiShelton   2021-02-27 14:17:34 opportunity      2

To calculate late a summary score, we will need to first group our data by standards again and then use the summarise function to create a new sentiment variable by adding all the positive and negative scores in the value column:

summary_afinn <- sentiment_afinn %>% 
  group_by(standards) %>% 
  summarise(sentiment = sum(value)) %>% 
  mutate(lexicon = "AFINN") %>%
  relocate(lexicon)

summary_afinn
## # A tibble: 2 × 3
##   lexicon standards sentiment
##   <chr>   <chr>         <dbl>
## 1 AFINN   ccss           -808
## 2 AFINN   ngss            503

Again, CCSS is overall negative while NGSS is overall positive!

✅ Comprehension Check

For your final task for this walkthough, calculate a single sentiment score for NGSS and CCSS using the remaining nrc and loughan lexicons and answer the following questions. Are these findings above still consistent?

Hint: The nrc lexicon contains “positive” and “negative” values just like bing and loughan, but also includes values like “trust” and “sadness” as shown below. You will need to use the filter() function to select rows that only contain “positive” and “negative.”

nrc
## # A tibble: 13,872 × 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     
## # ℹ 13,862 more rows
## # A tibble: 2 × 5
## # Groups:   standards [2]
##   standards method negative positive sentiment
##   <chr>     <chr>     <int>    <int>     <dbl>
## 1 ccss      nrc         766     2296      3.00
## 2 ngss      nrc          79      571      7.23
## # A tibble: 2 × 3
##   lexicon standards sentiment
##   <chr>   <chr>         <dbl>
## 1 AFINN   ccss           -808
## 2 AFINN   ngss            503

4. MODEL

As highlighted in Chapter 3 of Data Science in Education Using R, the Model step of the data science process entails “using statistical models, from simple to complex, to understand trends and patterns in the data.” The authors note that while descriptive statistics and data visualization during the Explore step can help us to identify patterns and relationships in our data, statistical models can be used to help us determine if relationships, patterns and trends are actually meaningful.

Recall from the PREPARE section that the Rosenberg et al. study was guide by the following questions:

  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?

Similar to our sentiment summary using the AFINN lexicon, the Rosenberg et al. study used the -5 to 5 sentiment score from the SentiStrength lexicon to answer RQ #1. To address the remaining questions the authors used a mixed effects model (also known as multi-level or hierarchical linear models via the lme4 package in R.

Collectively, the authors found that:

  1. The SentiStrength scale indicated an overall neutral sentiment for tweets about the Next Generation Science Standards.
  2. Teachers were more positive in their posts than other participants.
  3. Posts including #NGSSchat that were posted outside of chats were slightly more positive relative to those that did not include the #NGSSchat hashtag.
  4. The effect upon individuals of being involved in the #NGSSchat was positive, suggesting that there is an impact on individuals—not tweets—of participating in a community focused on the NGSS.
  5. Posts about the NGSS became substantially more positive over time.

5. COMMUNICATE

The final(ish) step in our workflow/process is sharing the results of analysis with wider audience. Krumm et al. (2018) outlined the following 3-step process for communicating with education stakeholders what you have learned through analysis:

  1. Select. Communicating what one has learned involves selecting among those analyses that are most important and most useful to an intended audience, as well as selecting a form for displaying that information, such as a graph or table in static or interactive form, i.e. a “data product.”
  2. Polish. After creating initial versions of data products, research teams often spend time refining or polishing them, by adding or editing titles, labels, and notations and by working with colors and shapes to highlight key points.
  3. Narrate. Writing a narrative to accompany the data products involves, at a minimum, pairing a data product with its related research question, describing how best to interpret the data product, and explaining the ways in which the data product helps answer the research question.

5a. Select

Remember that the questions of interest that we want to focus on our for our selection, polishing, and narration include:

  1. What is the public sentiment expressed toward the NGSS?
  2. How does sentiment for NGSS compare to sentiment for CCSS?

To address questions 1 and 2, I’m going to focus my analyses, data products and sharing format on the following:

  1. Analyses. For RQ1, I’m want to try and replicate as closely as possible the analysis by Rosenberg et al. so I will clean up my analysis and calculate a single sentiment score using the AFINN Lexicon for the entire tweet and label it positive or negative based on that score. I also want to highlight how regardless of the lexicon selected, NGSS tweets contain more positive words than negative, so I’ll also polish my previous analyses and calculate percentages of positive and negative words for the
  2. Data Products. I know these are shunned in the world of data viz, but I think a pie chart will actually be an effective way to quickly communicate the proportion of positive and negative tweets among the Next Generation Science Standards. And for my analyses with the bing, nrc, and loughan lexicons, I’ll create some 100% stacked bars showing the percentage of positive and negative words among all tweets for the NGSS and CCSS.
  3. Format. Similar to Lab 1, I’ll be using R Markdown again to create a quick slide deck. Recall that R Markdown files can also be used to create a wide range of outputs and formats, including polished PDF or Word documents, websites, web apps, journal articles, online books, interactive tutorials and more. And to make this process even more user-friendly, R Studio now includes a visual editor!

5b. Polish

NGSS Sentiment

I want to try and replicate as closely as possible the approach Rosenberg et al. used in their analysis. To do that, I’ll I can recycle some R code I used in section 2b. Tidy Text.

To polish my analyses and prepare, first I need to rebuild the tweets dataset from my ngss_tweets and ccss_tweets and select both the status_id that is unique to each tweet, and the text column which contains the actual post:

ngss_text <-
  ngss_tweets %>%
  filter(lang == "en") %>%
  select(status_id, text) %>%
  mutate(standards = "ngss") %>%
  relocate(standards)

ccss_text <-
  ccss_tweets %>%
  filter(lang == "en") %>%
  select(status_id, text) %>%
  mutate(standards = "ccss") %>%
  relocate(standards)

tweets <- bind_rows(ngss_text, ccss_text)

tweets
## # A tibble: 1,441 × 3
##    standards status_id           text                                           
##    <chr>     <chr>               <chr>                                          
##  1 ngss      1365716690336645124 "Switching gears for a bit for the \"Crosscutt…
##  2 ngss      1363217513761415171 "Was just introduced to the Engineering Habits…
##  3 ngss      1365709122763653133 "@IBchemmilam @chemmastercorey I’m familiar w/…
##  4 ngss      1365673294360420353 "@IBchemmilam @chemmastercorey How well does t…
##  5 ngss      1365667393188601857 "I am so honored and appreciative to have an o…
##  6 ngss      1365690477266284545 "Thank you @brian_womack I loved connecting wi…
##  7 ngss      1365706140496130050 "Please share #NGSSchat PLN! https://t.co/Qc2c…
##  8 ngss      1363669328147677189 "So excited about this weekend’s learning... p…
##  9 ngss      1365442786544214019 "The Educators Evaluating the Quality of Instr…
## 10 ngss      1364358149164175362 "Foster existing teacher social networks that …
## # ℹ 1,431 more rows

The status_id is important because like Rosenberg et al., I want to calculate an overall sentiment score for each tweet, rather than for each word.

Before I get that far however, I’ll need to tidy my tweets again and attach my sentiment scores.

Note that the closest lexicon we have available in our tidytext package to the SentiStrength lexicon used by Rosenberg is the AFINN lexicon which also uses a -5 to 5 point scale.

So let’s use unnest_tokens to tidy our tweets, remove stop words, and add afinn scores to each word similar to what we did in section 2c. Add Sentiment Values:

sentiment_afinn <- tweets %>%
  unnest_tokens(output = word, 
                input = text)  %>% 
  anti_join(stop_words, by = "word") %>%
  filter(!word == "amp") %>%
  inner_join(afinn, by = "word")

sentiment_afinn
## # A tibble: 1,540 × 4
##    standards status_id           word         value
##    <chr>     <chr>               <chr>        <dbl>
##  1 ngss      1365716690336645124 win              4
##  2 ngss      1365709122763653133 love             3
##  3 ngss      1365709122763653133 sweet            2
##  4 ngss      1365709122763653133 significance     1
##  5 ngss      1365667393188601857 honored          2
##  6 ngss      1365667393188601857 opportunity      2
##  7 ngss      1365667393188601857 wonderful        4
##  8 ngss      1365667393188601857 powerful         2
##  9 ngss      1365690477266284545 loved            3
## 10 ngss      1365706140496130050 share            1
## # ℹ 1,530 more rows

Next, I want to calculate a single score for each tweet. To do that, I’ll use the by now familiar group_by and summarize

afinn_score <- sentiment_afinn %>% 
  group_by(standards, status_id) %>% 
  summarise(value = sum(value))

afinn_score
## # A tibble: 857 × 3
## # Groups:   standards [2]
##    standards status_id           value
##    <chr>     <chr>               <dbl>
##  1 ccss      1362894990813188096     2
##  2 ccss      1362899370199445508     4
##  3 ccss      1362906588021989376    -2
##  4 ccss      1362910494487535618    -9
##  5 ccss      1362910913855160320    -1
##  6 ccss      1362928225379250179     2
##  7 ccss      1362933982074073090    -1
##  8 ccss      1362947497258151945    -3
##  9 ccss      1362949805694013446     3
## 10 ccss      1362970614282264583     3
## # ℹ 847 more rows

And like Rosenberg et al., I’ll add a flag for whether the tweet is “positive” or “negative” using the mutate function to create a new sentiment column to indicate whether that tweets was positive or negative.

To do this, we introduced the new if_else function from the dplyr package. This if_else function adds “negative” to the sentiment column if the score in the value column of the corresponding row is less than 0. If not, it will add a “positive” to the row.

afinn_sentiment <- afinn_score %>%
  filter(value != 0) %>%
  mutate(sentiment = if_else(value < 0, "negative", "positive"))

afinn_sentiment
## # A tibble: 820 × 4
## # Groups:   standards [2]
##    standards status_id           value sentiment
##    <chr>     <chr>               <dbl> <chr>    
##  1 ccss      1362894990813188096     2 positive 
##  2 ccss      1362899370199445508     4 positive 
##  3 ccss      1362906588021989376    -2 negative 
##  4 ccss      1362910494487535618    -9 negative 
##  5 ccss      1362910913855160320    -1 negative 
##  6 ccss      1362928225379250179     2 positive 
##  7 ccss      1362933982074073090    -1 negative 
##  8 ccss      1362947497258151945    -3 negative 
##  9 ccss      1362949805694013446     3 positive 
## 10 ccss      1362970614282264583     3 positive 
## # ℹ 810 more rows

Note that since a tweet sentiment score equal to 0 is neutral, I used the filter function to remove it from the dataset.

Finally, we’re ready to compute our ratio. We’ll use the group_by function and count the number of tweets for each of the standards that are positive or negative in the sentiment column. Then we’ll use the spread function to separate them out into separate columns so we can perform a quick calculation to compute the ratio.

afinn_ratio <- afinn_sentiment %>% 
  group_by(standards) %>% 
  count(sentiment) %>% 
  spread(sentiment, n) %>%
  mutate(ratio = negative/positive)

afinn_ratio
## # A tibble: 2 × 4
## # Groups:   standards [2]
##   standards negative positive ratio
##   <chr>        <int>    <int> <dbl>
## 1 ccss           421      211 2.00 
## 2 ngss            21      167 0.126

Finally,

afinn_counts <- afinn_sentiment %>%
  group_by(standards) %>% 
  count(sentiment) %>%
  filter(standards == "ngss")

afinn_counts %>%
ggplot(aes(x="", y=n, fill=sentiment)) +
  geom_bar(width = .6, stat = "identity") +
  labs(title = "Next Gen Science Standards",
       subtitle = "Proportion of Positive & Negative Tweets") +
  coord_polar(theta = "y") +
  theme_void()

NGSS vs CCSS

Finally, to address Question 2, I want to compare the percentage of positive and negative words contained in the corpus of tweets for the NGSS and CCSS standards using the four different lexicons to see how sentiment compares based on lexicon used.

I’ll begin by polishing my previous summaries and creating identical summaries for each lexicon that contains the following columns: method, standards, sentiment, and n, or word counts:

summary_afinn2 <- sentiment_afinn %>% 
  group_by(standards) %>% 
  filter(value != 0) %>%
  mutate(sentiment = if_else(value < 0, "negative", "positive")) %>% 
  count(sentiment, sort = TRUE) %>% 
  mutate(method = "AFINN")

summary_bing2 <- sentiment_bing %>% 
  group_by(standards) %>% 
  count(sentiment, sort = TRUE) %>% 
  mutate(method = "bing")

summary_nrc2 <- sentiment_nrc %>% 
  filter(sentiment %in% c("positive", "negative")) %>%
  group_by(standards) %>% 
  count(sentiment, sort = TRUE) %>% 
  mutate(method = "nrc") 

summary_loughran2 <- sentiment_loughran %>% 
  filter(sentiment %in% c("positive", "negative")) %>%
  group_by(standards) %>% 
  count(sentiment, sort = TRUE) %>% 
  mutate(method = "loughran")

Next, I’ll combine those four data frames together using the bind_rows function again:

summary_sentiment <- bind_rows(summary_afinn2,
                               summary_bing2,
                               summary_nrc2,
                               summary_loughran2) %>%
  arrange(method, standards) %>%
  relocate(method)

summary_sentiment
## # A tibble: 16 × 4
## # Groups:   standards [2]
##    method   standards sentiment     n
##    <chr>    <chr>     <chr>     <int>
##  1 AFINN    ccss      negative    740
##  2 AFINN    ccss      positive    477
##  3 AFINN    ngss      positive    278
##  4 AFINN    ngss      negative     45
##  5 bing     ccss      negative    926
##  6 bing     ccss      positive    446
##  7 bing     ngss      positive    230
##  8 bing     ngss      negative     66
##  9 loughran ccss      negative    433
## 10 loughran ccss      positive    112
## 11 loughran ngss      negative     73
## 12 loughran ngss      positive     57
## 13 nrc      ccss      positive   2296
## 14 nrc      ccss      negative    766
## 15 nrc      ngss      positive    571
## 16 nrc      ngss      negative     79

Then I’ll create a new data frame that has the total word counts for each set of standards and each method and join that to my summary_sentiment data frame:

total_counts <- summary_sentiment %>%
  group_by(method, standards) %>%
  summarise(total = sum(n))
## `summarise()` has grouped output by 'method'. You can override using the
## `.groups` argument.
sentiment_counts <- left_join(summary_sentiment, total_counts)
## Joining with `by = join_by(method, standards)`
sentiment_counts
## # A tibble: 16 × 5
## # Groups:   standards [2]
##    method   standards sentiment     n total
##    <chr>    <chr>     <chr>     <int> <int>
##  1 AFINN    ccss      negative    740  1217
##  2 AFINN    ccss      positive    477  1217
##  3 AFINN    ngss      positive    278   323
##  4 AFINN    ngss      negative     45   323
##  5 bing     ccss      negative    926  1372
##  6 bing     ccss      positive    446  1372
##  7 bing     ngss      positive    230   296
##  8 bing     ngss      negative     66   296
##  9 loughran ccss      negative    433   545
## 10 loughran ccss      positive    112   545
## 11 loughran ngss      negative     73   130
## 12 loughran ngss      positive     57   130
## 13 nrc      ccss      positive   2296  3062
## 14 nrc      ccss      negative    766  3062
## 15 nrc      ngss      positive    571   650
## 16 nrc      ngss      negative     79   650

Finally, I’ll add a new row that calculates the percentage of positive and negative words for each set of state standards:

sentiment_percents <- sentiment_counts %>%
  mutate(percent = n/total * 100)

sentiment_percents
## # A tibble: 16 × 6
## # Groups:   standards [2]
##    method   standards sentiment     n total percent
##    <chr>    <chr>     <chr>     <int> <int>   <dbl>
##  1 AFINN    ccss      negative    740  1217    60.8
##  2 AFINN    ccss      positive    477  1217    39.2
##  3 AFINN    ngss      positive    278   323    86.1
##  4 AFINN    ngss      negative     45   323    13.9
##  5 bing     ccss      negative    926  1372    67.5
##  6 bing     ccss      positive    446  1372    32.5
##  7 bing     ngss      positive    230   296    77.7
##  8 bing     ngss      negative     66   296    22.3
##  9 loughran ccss      negative    433   545    79.4
## 10 loughran ccss      positive    112   545    20.6
## 11 loughran ngss      negative     73   130    56.2
## 12 loughran ngss      positive     57   130    43.8
## 13 nrc      ccss      positive   2296  3062    75.0
## 14 nrc      ccss      negative    766  3062    25.0
## 15 nrc      ngss      positive    571   650    87.8
## 16 nrc      ngss      negative     79   650    12.2

Now that I have my sentiment percent summaries for each lexicon, I’m going great my 100% stacked bar charts for each lexicon:

sentiment_percents %>%
  ggplot(aes(x = standards, y = percent, fill=sentiment)) +
  geom_bar(width = .8, stat = "identity") +
  facet_wrap(~method, ncol = 1) +
  coord_flip() +
  labs(title = "Public Sentiment on Twitter", 
       subtitle = "The Common Core & Next Gen Science Standards",
       x = "State Standards", 
       y = "Percentage of Words")

And finished! The chart above clearly illustrates that regardless of sentiment lexicon used, the NGSS contains more positive words than the CCSS lexicon.

5c. Narrate

With our “data products” cleanup complete, we can start pulling together a quick presentation to share with the class. We’ve already seen what a more formal journal article looks like in the PREPARE section of this walkthrough. For your Independent Analysis for Lab 2, you’ll be creating either a simple report or slide deck to share out some key findings from our analysis.

Regardless of whether you plan to talk us through your analysis and findings with a presentation or walk us through with a brief written report, your assignment should address the following questions:

  1. Purpose. What question or questions are guiding your analysis? What did you hope to learn by answering these questions and why should your audience care about your findings?
  2. Methods. What data did you selected for analysis? What steps did you take took to prepare your data for analysis and what techniques you used to analyze your data? These should be fairly explicit with your embedded code.
  3. Findings. What did you ultimately find? How do your “data products” help to illustrate these findings? What conclusions can you draw from your analysis?
  4. Discussion. What were some of the strengths and weaknesses of your analysis? How might your audience use this information? How might you revisit or improve upon this analysis in the future?