Good bad

Gazzelloni F. (2023), Data Visualization: Wordcloud Good/Bad

# Load the required packages
library(wordcloud)
library(tm)
library(tidyverse)
library(tidytext)

# sample text dataset
load("text.RData")

# Create a corpus from the text dataset
corpus <- Corpus(VectorSource(text))

# Clean the corpus by removing stopwords and punctuation
corpus <- tm_map(corpus, removePunctuation)
corpus <- tm_map(corpus, removeWords, stopwords("english"))



# Create a term-document matrix from the corpus
tdm <- TermDocumentMatrix(corpus)

# Convert the term-document matrix to a matrix
m <- as.matrix(tdm)

# Get the frequency of each term in the matrix
v <- sort(rowSums(m), decreasing = TRUE)

# Create a data frame with the terms and their frequency
set.seed(1111)
df <- data.frame(word = names(v), freq = v) %>%
  inner_join(get_sentiments("bing"), by = "word") %>% #count(sentiment)
  filter(sentiment %in% c("positive",
                          "negative")) %>%
  mutate(
    color = ifelse(sentiment == "positive",
                   "#250c5f", "#cf5f26")) %>%
  sample_n(300)
  
                 
#summary(colors$freq)
png("wordcloud.png", res = 180)
wordcloud(df$word, 
          freq = df$freq, 
          ordered.colors = TRUE,
          random.color = FALSE,
          min.freq = 1,
          scale = c(5, 0.5), 
          colors = df$color)
dev.off()