Sentiment Analysis On Sst 5 Fine Grained
Metrics
Accuracy
Results
Performance results of various models on this benchmark
Comparison Table
| Model Name | Accuracy |
|---|---|
| an-algorithm-for-routing-capsules-in-all | 58.5 |
| bp-transformer-modelling-long-range-context | 52.71 |
| deep-contextualized-word-representations | 54.7 |
| emo2vec-learning-generalized-emotion | 43.6 |
| message-passing-attention-networks-for | 49.68 |
| a-la-carte-embedding-cheap-but-effective | 54.6 |
| improved-semantic-representations-from-tree | 51.0 |
| a-c-lstm-neural-network-for-text | 49.2 |
| recursive-deep-models-for-semantic | 45.7 |
| an-algorithm-for-routing-vectors-in-sequences | 59.8 |
| less-grammar-more-features | 49.6 |
| baseline-needs-more-love-on-simple-word | 46.1 |
| exploring-joint-neural-model-for-sentence | 44.82 |
| a-multi-sentiment-resource-enhanced-attention | 51.4 |
| self-explaining-structures-improve-nlp-models | 59.1 |
| learned-in-translation-contextualized-word | 53.7 |
| leveraging-multi-grained-sentiment-lexicon | 50.4 |
| convolutional-neural-networks-with-recurrent | 53.4 |
| lm-cppf-paraphrasing-guided-data-augmentation | 54.9 |
| cell-aware-stacked-lstms-for-modeling | 53.6 |
| emo2vec-learning-generalized-emotion | 41.6 |
| recursive-deep-models-for-semantic | 44.4 |
| sentiment-analysis-by-capsules | 49.3 |
| fine-grained-sentiment-classification-using | 55.5 |
| improved-sentence-modeling-using-suffix | 56.2 |
| fine-grained-sentiment-classification-using | 53.2 |
| 190600095 | 49.14 |
| all-but-the-top-simple-and-effective | 45.02 |
| star-transformer | 53.0 |