Utilizing BERT for Aspect-Based Sentiment Analysis via Constructing Auxiliary Sentence
Table 3: Performance on SentiHood dataset. We boldface the score with the best performance across all models. We use the results reported in Saeidi et al. (2016), Ma et al. (2018) and Liu et al. (2018). “-” means not reported.
['Model', 'Aspect  [ITALIC] Acc.', 'Aspect  [ITALIC] F1', 'Aspect AUC', 'Sentiment  [ITALIC] Acc.', 'Sentiment AUC']
More recently, the pre-trained language models, such as ELMo Peters et al. Especially, BERT has achieved excellent results in QA and NLI. We think this is due to the inappropriate use of the pre-trained BERT model. After we construct the auxiliary sentence, we can transform the TABSA task from a single sentence classification task to a sentence pair classification task. The results of the BERT-single model on aspect detection are better than Dmu-Entnet, but the accuracy of sentiment classification is much lower than that of both SenticLstm and Dmu-Entnet, with a difference of 3.8 and 5.5 respectively.