Aspect-Based Sentiment Analysis on Twitter
79.76AccuracyAPARN
Evaluation results
| Method | Links | |||
|---|---|---|---|---|
| APARNEncoder=BERT2026.05 | 79.76 | — | 78.79 | |
| AGCLEncoder=DeBERTa-Large2026.05 | 78.85 | — | 78.15 | |
| OTESGN2025.09 | 78.75 | 78.17 | — | |
| DAGCNEncoder=BERT2026.05 | 78.73 | — | 78.01 | |
| GHIEncoder=DeBERTa2026.05 | 78.73 | — | 77.72 | |
| PConvEncoder=RoBERTa2026.05 | 78.47 | — | 77.53 | |
| RCLEncoder=DeBERTa2026.05 | 78.32 | — | 77.47 | |
| RDGCN (2024)2025.09 | 78.29 | 77.14 | — | |
| LWEDA-ACT (2025)2025.09 | 78.17 | 77.16 | — | |
| dotGCNEncoder=BERT2026.05 | 78.11 | — | 77 | |
| SARLEncoder=RoBERTa2026.05 | 78.03 | — | 76.97 | |
| DGEDT (2020)2025.09 | 77.9 | 75.4 | — | |
| DGEDTEncoder=BERT2026.05 | 77.9 | — | 75.4 | |
| KumaGCNEncoder=BERT2026.05 | 77.89 | — | 77.03 | |
| S2GSLEncoder=BERT2026.05 | 77.84 | — | 77.11 | |
| TextGTEncoder=BERT2026.05 | 77.7 | — | 76.45 | |
| KDGN (2023)2025.09 | 77.64 | 75.55 | — | |
| MLFMEncoder=BERT2026.05 | 77.55 | — | 76.83 | |
| DualGCN (2021)2025.09 | 77.4 | 76.02 | — | |
| SSEGCN (2022)2025.09 | 77.4 | 76.02 | — | |
| DualGCNEncoder=BERT2026.05 | 77.4 | — | 76.02 | |
| SSEGCNEncoder=BERT2026.05 | 77.4 | — | 76.02 | |
| DAGFEncoder=RoBERTa2026.05 | 77.25 | — | 76.55 | |
| PWCNEncoder=RoBERTa2026.05 | 77.02 | — | 75.52 | |
| CSADGCN (2024)2025.09 | 76.81 | 75.67 | — | |
| SVCCL (2025)2025.09 | 76.72 | 75.92 | — | |
| IDGNN (2024)2025.09 | 76.7 | 75.9 | — | |
| TGCNEncoder=BERT2026.05 | 76.45 | — | 75.25 | |
| R-GATEncoder=BERT2026.05 | 76.15 | — | 74.88 | |
| BERT (2019)2025.09 | 75.92 | 75.18 | — | |
| SK-GCNEncoder=BERT2026.05 | 75 | — | 73.01 | |
| MWGCNEncoder=BERT2026.05 | 75 | — | 74.3 | |
| TNet (2018)2025.09 | 74.9 | 73.6 | — | |
| ASGCN (2019)2025.09 | 72.15 | 70.4 | — | |
| RAM (2017)2025.09 | 69.36 | 67.3 | — |