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Aspect Based Sentiment Analysis On Sentihood
Metrics
Aspect
Sentiment
Results
Performance results of various models on this benchmark
| Paper Title | Repository | |||
|---|---|---|---|---|
| BERT-pair-QA-B | 87.9 | 93.3 | Utilizing BERT for Aspect-Based Sentiment Analysis via Constructing Auxiliary Sentence | |
| BERT-pair-QA-M | 86.4 | 93.6 | Utilizing BERT for Aspect-Based Sentiment Analysis via Constructing Auxiliary Sentence | |
| Liu et al. | 78.5 | 91.0 | Recurrent Entity Networks with Delayed Memory Update for Targeted Aspect-based Sentiment Analysis | |
| 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 | - |
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