Knowing What, How and Why: A Near Complete Solution for Aspect-based Sentiment Analysis
Table 4: Stage one results of opinion term extraction.
['[EMPTY]', '14res P', '14res R', '14res F', '14lap P', '14lap R', '14lap F', '15res P', '15res R', '15res F', '16res P', '16res R', '16res F']
In terms of F score, our core model has again achieved the best performance compared with all existing baselines. Li-unified-R is generally not as good as our model on the restaurant datasets, but still performs very competitive and event better than our model on 14lap. Our–TG variant model has outperformed all baselines in the laptop domain. RINANTE, CMLA and IOG only learned the mutual influence of aspect and opinion term. Compared with these baseline models, our model learns the multi-lateral information flow among the three tasks, i.e., aspect extraction, sentiment classification and opinion term extraction. In the case of opinion term extraction, it would be relatively straightforward to locate opinion terms if their sentiment polarities are given. Specifically, the hOPT is used for unified tag prediction and thus the sentiment classification signals are backpropagated to BLSTMOPT, therefore, the opinion prediction can leverage such information.