This function searches for relevant words in a given text and adds sentiments labels (joy, anticipation, surprise, positive, trust, anger, sadness, fear, negative, disgust) for each of them, using NRC. Then, makes a summary for all words and plot results.
sentimentBreakdown(
text,
lang = "spanish",
exclude = c("maduro", "que"),
append_file = NA,
append_words = NA,
plot = TRUE,
subtitle = NA
)List. Contains data.frame with words and sentiments, summary and plot.
Character vector
Character. Language in text (used for stop words)
Character vector. Which word do you wish to exclude?
Character. Add a dictionary to append. This file must contain at least two columns, first with words and second with the sentiment (consider sentiments on description).
Dataframe. Same as append_file but appending data frame with word and sentiment directly
Boolean. Plot results summary?
Character. Add subtitle to the plot
Other Text Mining:
cleanText(),
ngrams(),
remove_stopwords(),
replaceall(),
textCloud(),
textFeats(),
textTokenizer(),
topics_rake()