sentimentBreakdown {lares}

Sentiment Breakdown on Text

Description

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.

Usage

sentimentBreakdown(
  text,
  lang = "spanish",
  exclude = c("maduro", "que"),
  append_file = NA,
  append_words = NA,
  plot = TRUE,
  subtitle = NA
)

Arguments

text

Character vector

lang

Character. Language in text (used for stop words)

exclude

Character vector. Which word do you wish to exclude?

append_file

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).

append_words

Dataframe. Same as append_file but appending data frame with word and sentiment directly

plot

Boolean. Plot results summary?

subtitle

Character. Add subtitle to the plot

Value

List. Contains data.frame with words and sentiments, summary and plot.

See Also

Other Text Mining: cleanText(), ngrams(), remove_stopwords(), replaceall(), textCloud(), textFeats(), textTokenizer(), topics_rake()