Sentiment Analysis
Sentiment Analysis is the task of classifying the polarity of a given text. For instance, a text-based tweet can be categorized into either "positive", "negative", or "neutral". Given the text and accompanying labels, a model can be trained to predict the correct sentiment.
Sentiment Analysis techniques can be categorized into machine learning approaches, lexicon-based approaches, and even hybrid methods. Some subcategories of research in sentiment analysis include: multimodal sentiment analysis, aspect-based sentiment analysis, fine-grained opinion analysis, language specific sentiment analysis.
More recently, deep learning techniques, such as RoBERTa and T5, are used to train high-performing sentiment classifiers that are evaluated using metrics like F1, recall, and precision. To evaluate sentiment analysis systems, benchmark datasets like SST, GLUE, and IMDB movie reviews are used.
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Papers
Showing 1–10 of 5630 papers
Benchmark Results
| # | Model | Metric | Claimed | Verified | Status |
|---|---|---|---|---|---|
| 1 | GPT-4o Fine-Tuned (Minimal) | Macro F1 | 86.99 | — | Unverified |
| 2 | GPT-4o-mini Fine-Tuned | Macro F1 | 86.77 | — | Unverified |
| 3 | GPT-4o-mini + ELECTRA Large FT (Prompt, Label) | Macro F1 | 83.49 | — | Unverified |
| 4 | GPT-4o + ELECTRA Large FT (Prompt, Label, Examples) | Macro F1 | 83.09 | — | Unverified |
| 5 | GPT-4o-mini + ELECTRA Base FT (Prompt, Label) | Macro F1 | 82.74 | — | Unverified |
| 6 | ELECTRA Large Fine-Tuned | Macro F1 | 82.36 | — | Unverified |
| 7 | GPT-4o + ELECTRA Large FT (Prompt, Label) | Macro F1 | 81.57 | — | Unverified |
| 8 | GPT-4o (Prompt) | Macro F1 | 80.14 | — | Unverified |
| 9 | GPT-4o-mini (Prompt) | Macro F1 | 79.52 | — | Unverified |
| 10 | ELECTRA Base Fine-Tuned | Macro F1 | 79.29 | — | Unverified |