ILWAANet: An Interactive Lexicon-Aware Word-Aspect Attention Network for aspect-level sentiment classification on social networking

Abstract

An Interactive Lexicon-Aware Word-Aspect Attention Network (ILWAAN) is proposed for aspect-level sentiment classification which deals with identifying the sentiment polarity of a specific aspect in its context and have potential application on social networking. In this model, effective multiple attention mechanisms (intra-attention and interactive-attention mechanisms) integrated with sentiment lexicon information are developed to form an aspect-specific representation at two levels: Phrase-level and Aggregation-level information. Specifically, an aspect and its context are fused with the sentiment lexicon information and learn their relationship representations by lexicon-aware attention operations. This allows the model to tries to incorporate the aspect information into the deep neural networks and learn to attend the correct sentiment context words conditioned on the informative aspect words. To evaluate the performance, we evaluate our model in three benchmark data: Twitter, Laptop, and Restaurant. The experimental results indicate that our models improve the performance for aspect-level sentiment classification.