基于多输入模型及句法结构的中文评论情感分析方法  

Chinese comment sentiment analysis method based on multi-input model and syntactic structure

作　　者：张宝华 张华平[1] 厉铁帅 商建云[1] ZHANG Baohua;ZHANG Huaping;LI Tieshuai;SHANG Jianyun(School of Computer Science&Technology,Beijing Institute of Technology,Beijing 100081,China;Politics and Law Commission of Central Military Commission of the People’s Republic of China,Beijing 100120,China)

机构地区：[1]北京理工大学计算机学院,北京100081 [2]中央军事委员会政法委员会,北京100120

出　　处：《大数据》2021年第6期41-52,共12页Big Data Research

基　　金：国家自然科学基金资助项目(No.61772075);北京市自然科学基金资助项目(No.4212026)。

摘　　要：海量的网络文本给情感分析任务带来了巨大的机遇和挑战,传统基于规则的方法已经很难胜任这类文本的分析工作,现有的深度学习方法存在一些不足,一方面模型的输入只包括文本嵌入矩阵,缺乏其他特征的使用;另一方面,词嵌入算法会导致文本结构信息缺失,进而影响分析效果。在对基于规则的情感分析方法中的句法规则进行研究的基础上,提出了一种结合MCNN、LSTM和全连接神经网络的多输入模型。同时在深度学习模型中构建了句法特征提取器来提取句法特征。在3个公开数据集上进行了实验,结果表明,构建的模型较其他模型拥有更好的分类性能,且句法规则特征的引入对模型的分类效果有一定的提升。Massive network texts have brought huge opportunities and challenges to sentiment analysis tasks.Traditional rule-based methods have been difficult to analyze such texts.Existing deep learning methods have some shortcomings.On the one hand,the inputs of the model only include the text embedding matrix,lack the use of other features.On the other hand,the algorithm of word embedding will lead to the lack of text structure information,then impact the result.Based on the research of syntactic rule in the rule-based sentiment analysis methods,a multi-input model combined with MCNN,LSTM and fully connected neural network was proposed.Meanwhile,a syntactic feature extractor to combine the syntactic features was constructed in the deep learning model.Experiments on three public data sets were conducted.The results show that the model constructed in this article has better classification performance than other models,and the introduction of syntactic rule features has a little improvement in the classification effect of the model.

关 键 词：情感分析 句法规则 多输入模型 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程]