Aspect Sentiment Classification with Aspect-Specific Opinion Spans

Aspect sentiment classification, predicting the sentiment polarity of given aspects, has drawn extensive attention. Previous attention-based models emphasize using aspect semantics to help extract opinion features for classification. However, these works are either not able to capture opinion spans as a whole or capture variable-length opinion spans. In this paper, we present a neat and effective multiple CRFs based structured attention model that is capable of extracting aspect-specific opinion spans. The sentiment polarity of the target is then classified based on the extracted opinion features and contextual information. The experimental results on four datasets demonstrate the effectiveness of the proposed model, and our further analysis shows that our model can capture aspect-specific opinion spans. 1