Integrating aspect-aware interactive attention and emotional position-aware for multi-aspect sentiment analysis

Abstract

Aspect-level Sentiment Analysis is a fine-grained sentiment analysis task, which aims to infer the corresponding sentiment polarity with different aspects in an opinion sentence. Attention-based neural networks have proven to be effective in extracting aspect terms, but the prior models are based on context-dependent. Moreover, the prior works only attend aspect terms to detect the sentiment word and cannot consider the sentiment words that might be influenced by domain-specific knowledge. In this work, we proposed a novel integrating Aspect-aware Interactive Attention and Emotional Position-aware module for multi-aspect sentiment analysis (abbreviated to AIAEP) where the aspect-aware interactive attention is utilized to extract aspect terms, and it fuses the domain-specific information of an aspect and context and learns their relationship representations by global context and local context attention mechanisms. Specifically, in the sentiment lexicon, the syntactic parse is used to increase the prior domain knowledge. Then we propose a novel position-aware fusion scheme to compose aspect-sentiment pairs. It combines absolute distance and relative distance from aspect terms and sentiment words, which can improve the accuracy of polarity classification. Extensive experimental results on SemEval2014 task4 restaurant and AIChallenge2018 datasets demonstrate that AIAEP can outperform state-of-the-art approaches, and it is very effective for aspect-level sentiment analysis.