Analyze Sentiment Using Tidytext Lexicons

Performs lexicon-based sentiment analysis on a DFM object using tidytext lexicons. Supports AFINN, Bing, and NRC lexicons with comprehensive scoring and emotion analysis. Now supports n-grams for improved negation and intensifier handling.

Usage

sentiment_lexicon_analysis(
  dfm_object,
  lexicon = "afinn",
  texts_df = NULL,
  feature_type = "words",
  ngram_range = 2,
  texts = NULL
)

Arguments

dfm_object

A quanteda DFM object (unigram or n-gram)

lexicon

Lexicon to use: "afinn", "bing", or "nrc" (default: "afinn")

texts_df

Optional data frame with original texts and metadata (default: NULL)

feature_type

Feature space: "words" (unigrams) or "ngrams" (default: "words")

ngram_range

N-gram size when feature_type = "ngrams" (default: 2 for bigrams)

texts

Optional character vector of texts for n-gram creation (default: NULL)

Value

A list containing:

document_sentiment

Data frame with sentiment scores per document

emotion_scores

Data frame with emotion scores (NRC only)

summary_stats

List of summary statistics

feature_type

Feature type used for analysis

Examples

if (FALSE) { # \dontrun{
corp <- quanteda::corpus(c("I love this!", "I hate that", "It's okay"))
dfm_obj <- quanteda::dfm(quanteda::tokens(corp))
results <- sentiment_lexicon_analysis(dfm_obj, lexicon = "afinn")
print(results$document_sentiment)

texts <- c("not good at all", "very happy indeed")
results_ngram <- sentiment_lexicon_analysis(
  dfm_obj,
  lexicon = "bing",
  feature_type = "ngrams",
  ngram_range = 2,
  texts = texts
)
} # }