Analyze Sentiment Using Tidytext Lexicons
Source:R/semantic_analysis.R
sentiment_lexicon_analysis.RdPerforms lexicon-based sentiment analysis on a DFM object using tidytext lexicons. Supports AFINN, Bing, and NRC lexicons with scoring and emotion analysis.
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)
#> # A tibble: 10 × 7
#> document positive negative total_sentiment_words sentiment_score
#> <chr> <dbl> <dbl> <dbl> <dbl>
#> 1 text1 3 0 3 1
#> 2 text10 0 3 3 -1
#> 3 text2 1 1 2 0
#> 4 text3 1 1 2 0
#> 5 text4 2 1 3 0.333
#> 6 text5 2 0 2 1
#> 7 text6 7 6 13 0.0769
#> 8 text7 1 1 2 0
#> 9 text8 12 1 13 0.846
#> 10 text9 3 4 7 -0.143
#> # ℹ 2 more variables: sentiment_raw <dbl>, sentiment <chr>
# }