Sentiment Classification Method Based on BLSTM and Aspect Attention Module
摘要
Aspect-Based Sentiment Analysis(ABSA) has been widely used in text information mining,but can hardly extract accurate feature information when the sentiment polarity of a sentence is fuzzy or a sentence has sentiment polarities of multiple aspects,which undermines performance of sentiment polarity classification.To address the problem,this paper proposes a sentiment classification method that combines the bidirectional long short-term memory and aspect attention module.The method uses multiple aspect attention modules to independently train different aspects at the same time,making information and attention operations of each aspect processed without affecting the other.Attention parameters of each aspect are independently learnt and modified,so hidden information of a specific aspect can be fully extracted for more effective recognition of sentiment polarities of different aspects.Experimental results on the SemEval dataset show that compared with the existing baseline sentiment analysis method,the proposed method can enhance sentiment classification performance,with the classification accuracy rate,recall rate and F1 value significantly improved.