How to Show Sentiment in Word Clouds using Q

Introduction

This post describes the basic process for creating such a Word Cloud in Q. Please read How to Show Sentiment in Word Clouds for a more general discussion of the logic behind the code below.

Requirements

A data file that contains a variable with the phrases you wish to use to create the Word Cloud.

This article uses data from tweets by President Trump

Method

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 Create > 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 using File > Data Sets > Add to Project > From R, and:

• Set the Name to trumpTweats
• Enter the code below.
• Press the play button (the blue triangle).
• Press Add data set and 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

• Create > Text Analysis > Advanced > Setup Text Analysis
• Select the Text Variable as text (this is the name of the variable containing the tweets)
• 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)
• Create > Text Analysis > Sentiment

Step 4: Sentiment for each word

• Create > R Output
• Paste in the code below
• Press Calculate and you will have the Word Cloud!

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
final.tokens = text.analysis.setup$final.tokens
td = t(vapply(flipTextAnalysis:::decodeNumericText(text.analysis.setup$transformed.tokenized), function(x) {
as.integer(final.tokens %in% x)
}, integer(length(final.tokens))))
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)

The results are as follows:

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