sentimentBreakdown: Sentiment Breakdown on Text

In lares: Analytics & Machine Learning Sidekick

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()

lares documentation built on Sept. 13, 2024, 1:08 a.m.