Aspect Based Sentiment Analysis On Sentihood
Metrics
Aspect
Sentiment
Results
Performance results of various models on this benchmark
Model Name | Aspect | Sentiment | Paper Title | Repository |
|---|---|---|---|---|
| Liu et al. | 78.5 | 91.0 | Recurrent Entity Networks with Delayed Memory Update for Targeted Aspect-based Sentiment Analysis | - |
| BERT-pair-QA-M | 86.4 | 93.6 | Utilizing BERT for Aspect-Based Sentiment Analysis via Constructing Auxiliary Sentence | - |
| BERT-pair-QA-B | 87.9 | 93.3 | Utilizing BERT for Aspect-Based Sentiment Analysis via Constructing Auxiliary Sentence | - |
| Sentic LSTM + TA + SA | 78.18 | 89.32 | Targeted Aspect-Based Sentiment Analysis via Embedding Commonsense Knowledge into an Attentive LSTM | - |
| LSTM-LOC | 69.3 | 81.9 | SentiHood: Targeted Aspect Based Sentiment Analysis Dataset for Urban Neighbourhoods | - |
0 of 5 row(s) selected.