基于注意力机制的评论情感分析及情感词检测

doi:10.11896/jsjkx.181002011

摘要/Abstract

摘要： 评论情感分析是用户生成内容分析的一个研究热点。评论对象的多样性与评论者用语的随意性,导致评论情感分析成为一个非常具有挑战性的任务。现有方法主要通过预先构建情感词表来计算评论的情感极性,但这类方法无法处理同一个词语在不同语境下情感极性存在差异的问题。针对这一问题,文中提出了一种基于注意力的卷积-递归神经网络模型,对评论的情感极性和词语在不同语境下的情感极性进行了建模。通过结合词语在句子中的上下文语境,所提方法可以将注意力集中在主要情感词周围的一个小范围内,并以一种自适应的方式对情感词的情感极性进行计算,提高了词语情感极性判断的准确率,进而提高了短文本的情感极性准确率。与CRNN,CNN以及基于情感词典的方法相比,所提方法在中文数据集(美团评论、党建评论)和英文数据集(亚马逊商品评论数据集)上都达到了更好的效果。

关键词: 多粒度, 卷积-递归神经网络, 情感分析, 注意力机制

Abstract: Comment sentiment analysis is one of the research hotspots in user generated content field.Because of the diversity of comment objects and the casualness of commentators’ language,comment sentiment analysis has become a challenging issue.The existing methods mainly calculate the emotional polarity of comments by pre-building the emotional vocabulary.However,these methods cannot adapt to the problem that the same words have different emotional polarities in different contexts.To overcome this problem,the attention based convolutional-recurrent neural network (A-CRNN) model was proposed to model the emotional polarity of comments and words in different contexts.By combining the context of words in sentences,the proposed method can focus attention on a small scale around the main emotional words.The A-CRNN model calculates the emotional polarity of the words through an adaptive method,which improves the accuracy of words’ emotional polarity judgment and the accuracy of short texts’ emotional polarity.Compared with CRNN,CNN and emotional dictionary methods,the proposed method achieves better results in Chinese dataset induding Meituan Review,Party Building Review and English dataset including Amazon Product Review.

Key words: Attention mechanism, Convolutional-recurrent neural network, Emotional analysis, Multi-granularity

中图分类号:

TP391

李苑,李智星,滕磊,王化明,王国胤. 基于注意力机制的评论情感分析及情感词检测[J]. 计算机科学, 2020, 47(1): 186-192. https://doi.org/10.11896/jsjkx.181002011

LI Yuan,LI Zhi-xing,TENG Lei,WANG Hua-ming,WANG Guo-yin. Comment Sentiment Analysis and Sentiment Words Detection Based on Attention Mechanism[J]. Computer Science, 2020, 47(1): 186-192. https://doi.org/10.11896/jsjkx.181002011

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