Transformer Based Multi-Grained Attention Network for Aspect-Based Sentiment Analysis
Jiahui Sun, Ping Han, Zheng Cheng, Enming Wu, Wenqing Wang
Abstract
Aspect-based sentiment analysis aims to predict sentiment polarity for every aspect in a sentence review. Most existing approaches are based on the sequence models, which may superimpose the emotional semantics of different tendencies and lack syntactic structure information. And most models adopt coarse-grained attention mechanism which still face the issues of weakness interaction between aspect and context. In this paper, we propose a transformer based multi-grained attention network (T-MGAN), which utilizes the Transformer module to learn the word-level representations of aspects and context respectively, and further utilizes the Tree Transformer module to obtain the phrase-level representations of contexts. It is capable of extracting the syntactic structure features and syntax information of aspect and context. In addition, we adopt dual-pooling method and multi-grained attention network to extract high quality aspect-context interactive representations. We evaluate the proposed model on three datasets and prove the effectiveness of the proposed model.