基于BLSTM与方面注意力模块的情感分类方法

基于方面的情感分析已广泛应用于文本信息挖掘,但在句子情感极性模糊或包含多个不同方面情感极性时难以准确提取特征信息,削弱了情感极性分类效果。为解决该问题,提出一种结合双向长短记忆网络和方面注意力模块的情感分类方法。利用多个方面注意力模块同时对不同方面进行独立训练,使每个方面信息与注意力操作互不影响,各自进行注意力参数的学习与调整,以充分提取特定方面的隐藏信息,从而更准确地识别不同方面的情感极性。在SemEval数据集上的实验结果表明,该方法相对现有的基准情感分析方法,可有效提升分类精确率、查全率与F1值,优化情感分类效果。

Sentiment Classification Method Based on BLSTM and Aspect Attention Module

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.