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 5501–5550 of 5630 papers
Benchmark Results
| # | Model | Metric | Claimed | Verified | Status |
|---|---|---|---|---|---|
| 1 | Word+ES (Scratch) | Attack Success Rate | 100 | — | Unverified |
| 2 | MT-DNN-SMART | Accuracy | 97.5 | — | Unverified |
| 3 | T5-11B | Accuracy | 97.5 | — | Unverified |
| 4 | MUPPET Roberta Large | Accuracy | 97.4 | — | Unverified |
| 5 | T5-3B | Accuracy | 97.4 | — | Unverified |
| 6 | ALBERT | Accuracy | 97.1 | — | Unverified |
| 7 | StructBERTRoBERTa ensemble | Accuracy | 97.1 | — | Unverified |
| 8 | XLNet (single model) | Accuracy | 97 | — | Unverified |
| 9 | SMARTRoBERTa | Dev Accuracy | 96.9 | — | Unverified |
| 10 | ELECTRA | Accuracy | 96.9 | — | Unverified |
| # | Model | Metric | Claimed | Verified | Status |
|---|---|---|---|---|---|
| 1 | RoBERTa-large with LlamBERT | Accuracy | 96.68 | — | Unverified |
| 2 | RoBERTa-large | Accuracy | 96.54 | — | Unverified |
| 3 | XLNet | Accuracy | 96.21 | — | Unverified |
| 4 | Heinsen Routing + RoBERTa Large | Accuracy | 96.2 | — | Unverified |
| 5 | RoBERTa-large 355M + Entailment as Few-shot Learner | Accuracy | 96.1 | — | Unverified |
| 6 | GraphStar | Accuracy | 96 | — | Unverified |
| 7 | DV-ngrams-cosine with NB sub-sampling + RoBERTa.base | Accuracy | 95.94 | — | Unverified |
| 8 | DV-ngrams-cosine + RoBERTa.base | Accuracy | 95.92 | — | Unverified |
| 9 | Roberta_Large ST + Cosine Similarity Loss | Accuracy | 95.9 | — | Unverified |
| 10 | BERT large finetune UDA | Accuracy | 95.8 | — | Unverified |
| # | Model | Metric | Claimed | Verified | Status |
|---|---|---|---|---|---|
| 1 | Llama-3.3-70B + CAPO | Accuracy | 62.27 | — | Unverified |
| 2 | Mistral-Small-24B + CAPO | Accuracy | 60.2 | — | Unverified |
| 3 | Heinsen Routing + RoBERTa Large | Accuracy | 59.8 | — | Unverified |
| 4 | RoBERTa-large+Self-Explaining | Accuracy | 59.1 | — | Unverified |
| 5 | Qwen2.5-32B + CAPO | Accuracy | 59.07 | — | Unverified |
| 6 | Heinsen Routing + GPT-2 | Accuracy | 58.5 | — | Unverified |
| 7 | BCN+Suffix BiLSTM-Tied+CoVe | Accuracy | 56.2 | — | Unverified |
| 8 | BERT Large | Accuracy | 55.5 | — | Unverified |
| 9 | LM-CPPF RoBERTa-base | Accuracy | 54.9 | — | Unverified |
| 10 | BCN+ELMo | Accuracy | 54.7 | — | Unverified |
| # | Model | Metric | Claimed | Verified | Status |
|---|---|---|---|---|---|
| 1 | Char-level CNN | Error | 4.88 | — | Unverified |
| 2 | SVDCNN | Error | 4.74 | — | Unverified |
| 3 | LEAM | Error | 4.69 | — | Unverified |
| 4 | fastText, h=10, bigram | Error | 4.3 | — | Unverified |
| 5 | SWEM-hier | Error | 4.19 | — | Unverified |
| 6 | SRNN | Error | 3.96 | — | Unverified |
| 7 | M-ACNN | Error | 3.89 | — | Unverified |
| 8 | DNC+CUW | Error | 3.6 | — | Unverified |
| 9 | CCCapsNet | Error | 3.52 | — | Unverified |
| 10 | Block-sparse LSTM | Error | 3.27 | — | Unverified |
| # | Model | Metric | Claimed | Verified | Status |
|---|---|---|---|---|---|
| 1 | Millions of Emoji | Training Time | 1,500 | — | Unverified |
| 2 | VLAWE | Accuracy | 93.3 | — | Unverified |
| 3 | RoBERTa-large 355M + Entailment as Few-shot Learner | Accuracy | 92.5 | — | Unverified |
| 4 | AnglE-LLaMA-7B | Accuracy | 91.09 | — | Unverified |
| 5 | byte mLSTM7 | Accuracy | 86.8 | — | Unverified |
| 6 | MEAN | Accuracy | 84.5 | — | Unverified |
| 7 | RNN-Capsule | Accuracy | 83.8 | — | Unverified |
| 8 | Capsule-B | Accuracy | 82.3 | — | Unverified |
| 9 | SuBiLSTM-Tied | Accuracy | 81.6 | — | Unverified |
| 10 | USE_T+CNN | Accuracy | 81.59 | — | Unverified |