Analyze Sentiment Using Tidytext Lexicons

Performs lexicon-based sentiment analysis on a DFM object using tidytext lexicons. Supports AFINN, Bing, and NRC lexicons with scoring and emotion analysis.

Tokens absent from the lexicon are ignored (not treated as neutral); summary_stats$token_match_rate reports the share of corpus tokens the lexicon covered. AFINN classification uses a +/- 0.5 band on the per-sentiment-word average (avg_sentiment); its n_sentiment_words column counts matched sentiment tokens, not document length.

Usage

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

Arguments

dfm_object

A quanteda DFM object (unigram)

lexicon

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

texts_df

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

feature_type

Feature space: "words" (unigrams). The AFINN/Bing/NRC lexicons are unigram lexicons; "ngrams" falls back to unigram scoring with a warning (default: "words").

ngram_range

Retained for backward compatibility; not used for scoring (default: 2)

texts

Optional character vector of texts used to rebuild a unigram DFM (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

# \donttest{
abstracts <- TextAnalysisR::SpecialEduTech$abstract[1:10]
corpus <- quanteda::corpus(abstracts)
dfm_object <- quanteda::dfm(quanteda::tokens(corpus))
lexicon_results <- sentiment_lexicon_analysis(dfm_object, lexicon = "bing")
print(lexicon_results$document_sentiment)
# }