Table 3 The experiments of state-of-the-art methods on aspect term level ABSA on all Chinese datasets
From: Attention-based Sentiment Reasoner for aspect-based sentiment analysis
Model | Camera | Car | Notebook | Phone | ||||
|---|---|---|---|---|---|---|---|---|
Acc. | F1 | Acc. | F1 | Acc. | F1 | Acc. | F1 | |
LSTM | 78.31 | 68.72 | 81.99 | 58.83 | 74.63 | 62.32 | 81.38 | 72.13 |
TD-LSTM | 70.48 | 51.46 | 76.53 | 46.67 | 67.10 | 40.58 | 69.17 | 53.40 |
AT-LSTM | 85.05 | 83.44 | 80.09 | 72.34 | 79.34 | 77.99 | 86.41 | 84.46 |
ATAE-LSTM | 85.54 | 84.09 | 81.90 | 76.88 | 83.47 | 82.14 | 85.77 | 83.87 |
MemNN | 70.59 | 55.13 | 75.55 | 51.01 | 69.10 | 53.51 | 70.29 | 55.93 |
ATAM-S | 82.88 | 72.50 | 82.94 | 64.18 | 75.59 | 60.09 | 84.86 | 75.35 |
ATAM-F | 88.30 | – | 82.94 | – | 77.52 | – | 88.46 | – |
AS-Reasoner | 89.71 | 88.66 | 85.52 | 79.88 | 85.95 | 84.41 | 89.17 | 88.02 |