基于细粒度信息感知BERT-EEP的情绪分类方法  

An emotion classification method based on fine-grained information perception BERT-EEP

作　　者：胡慧君[1] 杨雨烟 易洋 刘茂福[1] HU Hui-jun;YANG Yu-yan;YI Yang;LIU Mao-fu(School of Computer Science and Technology,Wuhan University of Science and Technology,Wuhan 430065,China)

机构地区：[1]武汉科技大学计算机科学与技术学院,湖北武汉430065

出　　处：《计算机工程与科学》2023年第4期751-760,共10页Computer Engineering & Science

基　　金：贵州省科技计划(黔科合后补助[2020]3003)。

摘　　要：网络舆情情绪分析专注于挖掘特定领域文本中深层次的情绪信息,对及时评估和化解舆情风险有重要意义。以往研究大多依赖情感符号、词性等基本情绪知识构建情绪语义特征,忽略了文本中情绪的持有者、线索等细粒度语言表达。为此,针对COVID-19疫情期间网络舆情数据的特点,引入同步双通道循环递归网络抽取细粒度情绪信息。在此基础上,提出辅助句构造法和基于BERT的情绪表达感知网络BERT-EEP,利用细粒度情绪信息辅助标签分类,并通过多头注意力机制和双向门控循环单元学习辅助信息和上下文之间的依赖关系,最终实现情绪分析。为评估所提方法的有效性,构建了一个具有细粒度表达的COVID-19中文情绪数据集。实验结果表明,所提方法能有效地融合细粒度情绪信息,在情绪分类任务上获得了优异的性能。Emotion analysis of Internet public opinion focuses on mining deep emotional information in do-main-specific texts,which is of great significance for timely assessment and resolving public opinion risks.Most of the previous work relied on basic emotion knowledge such as emotion symbols and lexical properties to construct semantic features of emotions,ignoring fine-grained linguistic emotion expressions such as holders and cues of emotions in texts.Therefore,according to the characteristics of Internet public opinion data during COVID-19,a synchronous dual-channel recurrent network is introduced to extract fine-grained emotional information.On this basis,an auxiliary sentence construction method and an emotional expression perceptive network based on BERT(BERT-EEP)are proposed,and fine-grained emotion information is used to assist label classification.The dependence relationship between auxiliary information and context is learned through multi-head attention mechanism and bidirectional gated loop unit to realize emotion analysis.To evaluate the validity of the proposed method,a COVID-19 Chinese mood dataset with fine-grained representation is constructed.Experimental results show that the proposed method can effectively perceive the fine-grained emotion information and achieve significant performance on the emotion classification task.

关 键 词：舆情分析 情绪分类 细粒度 情绪表达感知 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]