Hierarchical Gated Deep Memory Network With Position-Aware for Aspect-Based Sentiment Analysis

Aspect-based sentiment analysis aims at identifying the sentiment polarity of specific aspect in the sentence. Previous work has realized the importance of the information interaction between aspect term and context. However, most existing information interaction methods are coarse-grained, which results in a certain loss of information. In addition, most methods ignore the role of position information in identifying the sentiment polarity of the aspect. To better address the two problems, we propose a novel approach, called hierarchical gated deep memory network with position-aware. Our approach has two characteristics: 1) it has fine-grained information interaction attention mechanism which models the word-level interaction between aspect and context. The sentence-to-aspect attention is used to capture the most indicative sentiment words in context. And the aspect-to-sentence attention is used to capture the most important word in the aspect term. 2) The position information is embedded as a feature in the sentence representation. Finally, we conduct sentiment classification comparative experiment on laptop and restaurant datasets. The experimental results show that our model achieves state-of-the-art performance on aspect-based sentiment analysis.