SentiLR: Linguistic Knowledge Enhanced Language Representation for Sentiment Analysis
Table 2: F1, accuracy (Acc.) and Macro-F1 (MF1.) on four aspect-level sentiment analysis tasks including aspect term extraction (ATE), aspect term sentiment classification (ATSC), aspect category detection (ACD) and aspect category sentiment classification (ACSC) (%).
['Task Dataset', 'ATE Lap14', 'ATE Res14', 'ATSC Lap14', 'ATSC Lap14', 'ATSC Res14', 'ATSC Res14', 'ACD Res14', 'ACD Res16', 'ACSC Res14', 'ACSC Res14', 'ACSC Res16', 'ACSC Res16']
We can see that SentiLR-B / SentiLR-R outperforms the baselines on most of the datasets. Interestingly, in addition to aspect-level sentiment classification, our model also performs well in aspect term extraction and aspect category detection. Since the aspect words are mostly nouns, part-of-speech tag may provide additional knowledge for the extraction task. Furthermore, the aspect terms can be signaled by neighboring sentiment words. This may explain why our sentiment-aware representation can help to extract / detect the aspect term / category.