How to Show Sentiment in Word Clouds using Displayr
The Word Cloud above summarizes some data from tweets by President Trump. The green words are words that are significantly more likely to be used in tweets with a positive sentiment. The red represents words more likely to be used in negative tweets. This post describes the basic process for creating such a Word Cloud in Displayr. Please read How to Show Sentiment in Word Clouds for a more general discussion of the logic behind the code below.
Step 1: Importing the data
This post assumes that you have already imported a data file and this data file contains a variable that contains the phrases that you wish to use to create the Word Cloud. If you have the data in some other format, instead use Insert > R Output and use the code and instructions described in How to Show Sentiment in Word Clouds using R.
If you want to reproduce the Word Cloud form above, you can do so by pressing Insert > Data Set (data), clicking on R, and
- Set the Name to trumpTweats
- Enter the code below.
- Press OK.
load(url("http://varianceexplained.org/files/trump_tweets_df.rda")) trump_tweets_df$text <- gsub("http.*", "", trump_tweets_df$text) trump_tweets_df
Step 2: Extracting the words
- Insert > More (Analysis) > Setup Text Analysis
- Select the Text Variable as text (this is the name of the variable containing the tweats)
- Check the Automatic option at the top.
Step 3: Sentiment for the phrases (tweets)
- Go to the Variables and Questions tab
- Select the first variable (it is called text)
- Insert > More (Analysis) > Techniques > Save Sentiment Scores
Step 4: Sentiment for each word
- Create > R Output
- Paste in the code below
As discussed in How to Show Sentiment in Word Clouds , your Word Cloud may look a bit different and you do need to perform a check to make sure no long words are missing. Also, if you have tried these steps a few times in the same project, you will need to update the variable, R Output, and question names to make everything work.
# Sentiment analysis of the phrases phrase.sentiment = `Sentiment scores from text.analysis.setup` phrase.sentiment[phrase.sentiment >= 1] = 1 phrase.sentiment[phrase.sentiment <= -1] = -1 # Sentiment analysis of the words td = as.matrix(AsTermMatrix(text.analysis.setup, min.frequency = 1.0, sparse = TRUE)) counts = text.analysis.setup$final.counts phrase.word.sentiment = sweep(td, 1, phrase.sentiment, "*") phrase.word.sentiment[td == 0] = NA # Setting missing values to Missing word.mean = apply(phrase.word.sentiment,2, FUN = mean, na.rm = TRUE) word.sd = apply(phrase.word.sentiment,2, FUN = sd, na.rm = TRUE) word.n = apply(!is.na(phrase.word.sentiment),2, FUN = sum, na.rm = TRUE) word.se = word.sd / sqrt(word.n) word.z = word.mean / word.se word.z[word.n <= 3 || is.na(word.se)] = 0 words = text.analysis.setup$final.tokens x = data.frame(word = words, freq = counts, "Sentiment" = word.mean, "Z-Score" = word.z, Length = nchar(words)) word.data = x[order(counts, decreasing = TRUE), ] # Working out the colors n = nrow(word.data) colors = rep("grey", n) colors[word.data$Z.Score < -1.96] = "Red" colors[word.data$Z.Score > 1.96] = "Green" # Creating the word cloud library(wordcloud2) wordcloud2(data = word.data[, -3], color = colors, size = 0.4)