Lesson 3. Text Mining Twitter Data With TidyText in R

Learning Objectives

After completing this tutorial, you will be able to:

  • Clean or “munge” social media data to prepare it for analysis.
  • Use the tidytext package in R to explore and analyze word counts associated with tweets.

What You Need

You will need a computer with internet access to complete this lesson.

In this lesson you will dive deeper into using twitter to understand a particular topic or event. You will learn more about text mining.

Data Munging 101

When you work with data from sources like NASA, USGS, etc. there are particular cleaning steps that you often need to do. For instance:

  • you may need to remove nodata values
  • you may need to scale the data
  • and more

However, the data generally have a set structure in terms of file formats and metadata.

When you work with social media and other text data the user community creates and curates the content. This means there are NO RULES! This also means that you may have to perform extra steps to clean the data to ensure you are analyzing the right thing.

Next, let’s look at a different workflow - exploring the actual text of the tweets which will involve some text mining.

In this example, let’s find tweets that are using the words “forest fire” in them.

First, you load the rtweet and other needed R packages. Note you are introducing 2 new packages lower in this lesson: igraph and ggraph.

# load twitter library - the rtweet library is recommended now over twitteR
# plotting and pipes - tidyverse!
# text mining library
# plotting packages
climate_tweets <- search_tweets(q = "#climatechange", n = 10000,
                                      lang = "en",
                                      include_rts = FALSE)

Let’s look at the results. Note any issues with your data? It seems like when you search for forest fire, you get tweets that contain the words forest and fire in them - but these tweets are not necessarily all related to your science topic of interest. Or are they?

If you set your query to q="forest+fire" rather than forest fire then the API fill find tweets that use the words together in a string rathen than across the entire string. Let’s try it.

# Find tweet using forest fire in them
climate_tweets <- search_tweets(q = "#climatechange", n = 10000, lang = "en",
                             include_rts = FALSE)
# check data to see if there are emojis
## [1] "Thank you @StanleyPJohnson for bringing #wildlife + #biodiversity loss into @BBCNewsnight debate on #environment, &amp;… https://t.co/r0PKh7lYX1"
## [2] "NGO´s Urge Shift To 100% Renewable Energy: https://t.co/8Z53igdfW5 #climatechange #climateaction https://t.co/oyye6Blepo"                        
## [3] "#CostaRica #Ambassador Roman Macaya Speaks About Importance of #Science #Diplomacy in 2018- #climatechange... https://t.co/LHZNxeRdUv"           
## [4] "\"If you truly love nature, you will find beauty everywhere\". ~Vincent van Gogh https://t.co/I7Y5LKgxnf… https://t.co/aHz23p0885"               
## [5] "Currently watching the movie, #DayAfterTomorrow on @AMC_TV If this isn’t what is playing out in our world currently… https://t.co/6nFrWXyp23"    
## [6] "The latest The climate Daily! https://t.co/lF4wusZXEB Thanks to @Gail7175 @nordstjarna @setyourmindfree #climate #climatechange"

Data Clean-Up

Looking at the data above, it becomes clear that there is a lot of clean-up associated with social media data.

First, there are url’s in your tweets. If you want to do a text analysis to figure out what words are most common in your tweets, the URL’s won’t be helpful. Let’s remove those.

# remove urls tidyverse is failing here for some reason
# climate_tweets %>%
#  mutate_at(c("stripped_text"), gsub("http.*","",.))

# remove http elements manually
climate_tweets$stripped_text <- gsub("http.*","",  climate_tweets$text)
climate_tweets$stripped_text <- gsub("https.*","", climate_tweets$stripped_text)

Finally, you can clean up your text. If you are trying to create a list of unique words in your tweets, words with capitalization will be different from words that are all lowercase. Also you don’t need punctuation to be returned as a unique word.

# note the words that are recognized as unique by R
a_list_of_words <- c("Dog", "dog", "dog", "cat", "cat", ",")
## [1] "Dog" "dog" "cat" ","

You can use the tidytext::unnest_tokens() function in the tidytext package to magically clean up your text! When you use this function the following things will be cleaned up in the text:

  1. Convert text to lowercase: each word found in the text will be converted to lowercase so ensure that you don’t get duplicate words due to variation in capitalization.
  2. Punctuation is removed: all instances of periods, commas etc will be removed from your list of words , and
  3. Unique id associated with the tweet: will be added for each occurrence of the word

The unnest_tokens() function takes two arguments:

  1. The name of the column where the unique word will be stored and
  2. The column name from the data.frame that you are using that you want to pull unique words from.

In your case, you want to use the stripped_text column which is where you have your cleaned up tweet text stored.

# remove punctuation, convert to lowercase, add id for each tweet!
climate_tweets_clean <- climate_tweets %>%
  dplyr::select(stripped_text) %>%
  unnest_tokens(word, stripped_text)

Now you can plot your data. What do you notice?

# plot the top 15 words -- notice any issues?
climate_tweets_clean %>%
  count(word, sort = TRUE) %>%
  top_n(15) %>%
  mutate(word = reorder(word, n)) %>%
  ggplot(aes(x = word, y = n)) +
  geom_col() +
  xlab(NULL) +
  coord_flip() +
      labs(x = "Count",
      y = "Unique words",
      title = "Count of unique words found in tweets")

plot of users tweeting about fire.

You plot of unique words contains some words that may not be useful to use. For instance “a” and “to”. In the word of text mining you call those words - ‘stop words’. You want to remove these words from your analysis as they are fillers used to compose a sentence.

Lucky for use, the tidytext package has a function that will help us clean up stop words! To use this you:

  1. Load the stop_words data included with tidytext. This data is simply a list of words that you may want to remove in a natural language analysis.
  2. Then you use anti_join to remove all stop words from your analysis.

Let’s give this a try next!

# load list of stop words - from the tidytext package
# view first 6 words
## # A tibble: 6 x 2
##        word lexicon
##       <chr>   <chr>
## 1         a   SMART
## 2       a's   SMART
## 3      able   SMART
## 4     about   SMART
## 5     above   SMART
## 6 according   SMART

## [1] 133650

# remove stop words from your list of words
cleaned_tweet_words <- climate_tweets_clean %>%

# there should be fewer words now
## [1] 74490

Now that you’ve performed this final step of cleaning, you can try to plot, once again.

# plot the top 15 words -- notice any issues?
cleaned_tweet_words %>%
  count(word, sort = TRUE) %>%
  top_n(15) %>%
  mutate(word = reorder(word, n)) %>%
  ggplot(aes(x = word, y = n)) +
  geom_col() +
  xlab(NULL) +
  coord_flip() +
      labs(y = "Count",
      x = "Unique words",
      title = "Count of unique words found in tweets",
      subtitle = "Stop words removed from the list")

top 15 words used in tweets

Explore Networks of Words

You might also want to explore words that occur together in tweets. LEt’s do that next.

ngrams specifies pairs and 2 is the number of words together

# library(devtools)

# remove punctuation, convert to lowercase, add id for each tweet!
climate_tweets_paired_words <- climate_tweets %>%
  dplyr::select(stripped_text) %>%
  unnest_tokens(paired_words, stripped_text, token = "ngrams", n = 2)

climate_tweets_paired_words %>%
  count(paired_words, sort = TRUE)
## # A tibble: 62,682 x 2
##        paired_words     n
##               <chr> <int>
##  1   climate change  1282
##  2           in the   636
##  3           of the   403
##  4       the arctic   341
##  5 climatechange is   299
##  6       learn more   298
##  7        more here   279
##  8 of climatechange   250
##  9             is a   212
## 10           on the   209
## # ... with 62,672 more rows
climate_tweets_separated_words <- climate_tweets_paired_words %>%
  separate(paired_words, c("word1", "word2"), sep = " ")

climate_tweets_filtered <- climate_tweets_separated_words %>%
  filter(!word1 %in% stop_words$word) %>%
  filter(!word2 %in% stop_words$word)

# new bigram counts:
climate_words_counts <- climate_tweets_filtered %>%
  count(word1, word2, sort = TRUE)

## # A tibble: 6 x 3
##           word1         word2     n
##           <chr>         <chr> <int>
## 1       climate        change  1282
## 2 climatechange globalwarming   146
## 3        global       warming   132
## 4           sea       turtles   113
## 5        sahara        desert    85
## 6        arctic       climate    81

Finally, plot the data


# plot climate change word network
climate_words_counts %>%
        filter(n >= 24) %>%
        graph_from_data_frame() %>%
        ggraph(layout = "fr") +
        geom_edge_link(aes(edge_alpha = n, edge_width = n)) +
        geom_node_point(color = "darkslategray4", size = 3) +
        geom_node_text(aes(label = name), vjust = 1.8, size = 3) +
        labs(title = "Word Network: Tweets using the hashtag - Climate Change",
             subtitle = "Text mining twitter data ",
             x = "", y = "")

word associations for climate change tweets

You expect the words climate & change to have a high

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