Aspect-Term Sentiment Analysis on LAPTOP SemEval 2014 (test)
79.15Macro-F1BiSyn-GAT
Evaluation results
| Method | Links | ||||
|---|---|---|---|---|---|
| BiSyn-GAT2025.12 | 79.15 | 82.44 | — | — | |
| BERT-ADA JointTrain Dataset=Lapt. + Rest., Train Type=Joint2019.08 | 78.74 | 79.94 | — | — | |
| SDGCN-BERTTrain Dataset=Laptops, Train Type=In-domain2019.08 | 78.34 | 81.35 | — | — | |
| dotGCNCategory=Labeled Data Dependent Methods2023.10 | 78.1 | 81 | — | — | |
| dotGCN2025.12 | 78.1 | 81.03 | — | — | |
| Dual GCN2025.12 | 78.1 | 81.8 | — | — | |
| SSEGCN2025.12 | 77.96 | 81.01 | — | — | |
| KE-IGCN2025.12 | 77.89 | 81.06 | — | — | |
| XLNet-baseTrain Dataset=Laptops, Train Type=In-domain2019.08 | 77.78 | 79.89 | — | — | |
| CMV-Fuse2025.12 | 77.75 | 81.16 | — | — | |
| XLNet-baseTrain Dataset=Lapt. + Rest., Train Type=Joint2019.08 | 76.92 | 80.88 | — | — | |
| P-SUMepochs=42020.10 | 76.81 | 79.55 | — | — | |
| H-SUMepochs=42020.10 | 76.52 | 79.4 | — | — | |
| BAT2020.10 | 76.5 | 79.35 | — | — | |
| BERT-PTepochs=302020.10 | 76.47 | 79.48 | — | — | |
| AEN-BERTTrain Dataset=Laptops, Train Type=In-domain2019.08 | 76.31 | 79.93 | — | — | |
| BERT-ADA LaptTrain Dataset=Lapt. + Rest., Train Type=Joint2019.08 | 75.77 | 80.23 | — | — | |
| DGEDTCategory=Labeled Data Dependent Methods2023.10 | 75.6 | 79.8 | — | — | |
| BERT-PTTrain Dataset=Laptops, Train Type=In-domain2019.08 | 75.08 | 78.07 | — | — | |
| BERT-PT2020.10 | 75.08 | 78.07 | — | — | |
| BERT-SPCTrain Dataset=Laptops, Train Type=In-domain2019.08 | 75.03 | 78.99 | — | — | |
| BERT-PT*epochs=4, model_selection=proposed2020.10 | 75.03 | 78.21 | — | — | |
| BERT-ADA RestTrain Dataset=Lapt. + Rest., Train Type=Joint2019.08 | 74.93 | 79.14 | — | — | |
| BERT-baseTrain Dataset=Lapt. + Rest., Train Type=Joint2019.08 | 74.47 | 78.81 | — | — | |
| BERT-ADA LaptTrain Dataset=Laptops, Train Type=In-domain2019.08 | 74.18 | 79.19 | — | — | |
| BERT-ADA JointTrain Dataset=Laptops, Train Type=In-domain2019.08 | 74.18 | 78.96 | — | — | |
| BERT-ADA RestTrain Dataset=Laptops, Train Type=In-domain2019.08 | 74.09 | 78.6 | — | — | |
| R-GAT2025.12 | 74.07 | 78.21 | — | — | |
| BERT-ADA LaptTrain Dataset=Restaurants, Train Type=Cross-domain2019.08 | 72.99 | 77.92 | — | — | |
| BERT-baseTrain Dataset=Laptops, Train Type=In-domain2019.08 | 72.6 | 77.69 | — | — | |
| BERT2025.12 | 72.38 | 77.58 | — | — | |
| TNet-AS (ASVAET)Classifier=TNet-AS, Variant=ASVAET2018.10 | 72.31 | 77.57 | — | — | |
| XLNet-baseTrain Dataset=Restaurants, Train Type=Cross-domain2019.08 | 72.24 | 77.78 | — | — | |
| BERT2020.10 | 71.91 | 75.29 | — | — | |
| TNet-ASClassifier=TNet-AS, Variant=Standard2018.10 | 71.88 | 76.75 | — | — | |
| TNet-AS (ST)Classifier=TNet-AS, Variant=ST2018.10 | 71.74 | 76.88 | — | — | |
| TNet-AS (EMB)Classifier=TNet-AS, Variant=EMB2018.10 | 71.52 | 76.45 | — | — | |
| BERT-baseTrain Dataset=Restaurants, Train Type=Cross-domain2019.08 | 70.78 | 75.86 | — | — | |
| BILSTM-ATT-G (ASVAET)Classifier=BILSTM-ATT-G, Variant=ASVAET2018.10 | 70.52 | 75.44 | — | — | |
| BERT-ADA RestTrain Dataset=Restaurants, Train Type=Cross-domain2019.08 | 70.46 | 76.16 | — | — | |
| BILSTM-ATT-G (ST)Classifier=BILSTM-ATT-G, Variant=ST2018.10 | 70.31 | 74.7 | — | — | |
| BERT-ADA JointTrain Dataset=Restaurants, Train Type=Cross-domain2019.08 | 69.84 | 75.91 | — | — | |
| BILSTM-ATT-GClassifier=BILSTM-ATT-G, Variant=Standard2018.10 | 69.54 | 74.26 | — | — | |
| IAN (ASVAET)Classifier=IAN, Variant=ASVAET2018.10 | 69.39 | 74.02 | — | — | |
| IAN (ST)Classifier=IAN, Variant=ST2018.10 | 68.25 | 73.25 | — | — | |
| BILSTM-ATT-G (EMB)Classifier=BILSTM-ATT-G, Variant=EMB2018.10 | 68.25 | 73.61 | — | — | |
| COLACategory=Zero-shot Methods2023.10 | 67.5 | 87 | — | — | |
| GPT-3.5 TurboCategory=Zero-shot Methods2023.10 | 66.7 | 85 | — | — | |
| MemNet (ASVAET)Classifier=MemNet, Variant=ASVAET2018.10 | 65.88 | 73.21 | — | — | |
| IAN (EMB)Classifier=IAN, Variant=EMB2018.10 | 65.27 | 70.89 | — | — | |
| MemNet (EMB)Classifier=MemNet, Variant=EMB2018.10 | 65.06 | 72.17 | — | — | |
| MemNet (ST)Classifier=MemNet, Variant=ST2018.10 | 64.39 | 69.52 | — | — | |
| MemNetClassifier=MemNet, Variant=Standard2018.10 | 64.38 | 70.28 | — | — | |
| TC-LSTM (ASVAET)Classifier=TC-LSTM, Variant=ASVAET2018.10 | 64.23 | 70.04 | — | — | |
| IANClassifier=IAN, Variant=Standard2018.10 | 62.9 | 69.48 | — | — | |
| TC-LSTM (ST)Classifier=TC-LSTM, Variant=ST2018.10 | 62.54 | 68.47 | — | — | |
| TC-LSTMClassifier=TC-LSTM, Variant=Standard2018.10 | 62.42 | 68.42 | — | — | |
| BERT+GRUBackbone=bert-base-uncased2019.10 | 61.12 | — | 61.88 | 60.47 | |
| BERT+TFMBackbone=bert-base-uncased2019.10 | 60.8 | — | 63.23 | 58.64 | |
| BERT+CRFBackbone=bert-base-uncased2019.10 | 60.78 | — | 62.22 | 59.49 | |
| BERT+SANBackbone=bert-base-uncased2019.10 | 60.49 | — | 62.42 | 58.71 | |
| BERT+LinearBackbone=bert-base-uncased2019.10 | 60.43 | — | 62.16 | 58.9 | |
| Luo et al., 2019Model Category=Existing Models2019.10 | 60.35 | — | — | — | |
| TC-LSTM (EMB)Classifier=TC-LSTM, Variant=EMB2018.10 | 60.31 | 67.51 | — | — | |
| He et al., 2019Model Category=Existing Models2019.10 | 58.37 | — | — | — | |
| Li et al., 2019aModel Category=Existing Models2019.10 | 57.9 | — | 61.27 | 54.89 | |
| Liu et al., 2018Model Category=LSTM-CRF2019.10 | 56.19 | — | 53.31 | 59.4 | |
| Ma and Hovy, 2016Model Category=LSTM-CRF2019.10 | 54.71 | — | 58.66 | 51.26 | |
| Lample et al., 2016Model Category=LSTM-CRF2019.10 | 54.24 | — | 58.61 | 50.47 | |
| AE-LSTMClassifier=-2018.10 | — | 68.9 | — | — | |
| ATAE-LSTMClassifier=-2018.10 | — | 68.7 | — | — | |
| CNN-ASPClassifier=-2018.10 | — | 72.46 | — | — | |
| GCAEClassifier=-2018.10 | — | 69.14 | — | — |