Performs sentiment analysis using transformer-based embeddings and neural models. This approach uses pre-trained language models for contextual sentiment detection without requiring sentiment lexicons. Particularly effective for handling:
Complex contextual sentiment
Implicit sentiment and sarcasm
Domain-specific sentiment
Negation and intensifiers (automatically handled by the model)
Usage
sentiment_embedding_analysis(
texts,
embeddings = NULL,
model_name = "distilbert-base-uncased-finetuned-sst-2-english",
doc_names = NULL,
use_gpu = FALSE
)Arguments
- texts
Character vector of texts to analyze
- embeddings
Optional pre-computed embedding matrix (from generate_embeddings)
- model_name
Sentiment model name (default: "distilbert-base-uncased-finetuned-sst-2-english")
- doc_names
Optional document names/IDs
- use_gpu
Whether to use GPU if available (default: FALSE)
Value
A list containing:
- document_sentiment
Data frame with document-level sentiment scores and classifications
- emotion_scores
NULL (emotion detection not currently supported for embeddings)
- summary_stats
Summary statistics including document counts and average scores
- model_used
Name of the transformer model used
- feature_type
"embeddings"
Examples
if (FALSE) { # \dontrun{
texts <- c(
"The results significantly improved student outcomes.",
"The intervention showed no clear benefit.",
"Students reported difficulty with the material."
)
result <- sentiment_embedding_analysis(texts)
print(result$document_sentiment)
print(result$summary_stats)
} # }