基于多输入模型及句法结构的中文评论情感分析方法

张宝华; 张华平; 厉铁帅; 商建云

doi:10.11959/j.issn.2096-0271.2021059

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基于多输入模型及句法结构的中文评论情感分析方法

专题：大数据支撑的智能应用 | 更新时间：2024-06-03

- 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
- 大数据 2021年7卷第6期页码：41-52
- 作者机构：
  
  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
- 作者简介：
  
  [ "张宝华（1996- ），男，北京理工大学计算机学院硕士生，主要研究方向为自然语言处理、情感分析" ]
  [ "张华平（1978- ），男，博士，北京理工大学计算机学院副研究员，主要研究方向为大数据搜索与挖掘、自然语言处理、社交网络" ]
  [ "厉铁帅（1975- ），男，中央军事委员会政法委员会高级工程师，主要研究方向为计算机应用" ]
  [ "商建云（1965- ），女，博士，北京理工大学计算机学院高级工程师，主要研究方向为自然语言处理、数据挖掘" ]
- 基金信息：
  
  国家自然科学基金资助项目;The National Natural Science Foundation of China(61772075);北京市自然科学基金资助项目;Beijing Municipal Natural Science Foundation(4212026)
- DOI：10.11959/j.issn.2096-0271.2021059
  中图分类号： TP183
- 网络首发：2021-11，
  
  纸质出版：2021-11-15
- 稿件说明：
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张宝华, 张华平, 厉铁帅, 等. 基于多输入模型及句法结构的中文评论情感分析方法[J]. 大数据, 2021,7(6):41-52.

Baohua ZHANG, Huaping ZHANG, Tieshuai LI, et al. Chinese comment sentiment analysis method based on multi-input model and syntactic structure[J]. Big data research, 2021, 7(6): 41-52.
张宝华, 张华平, 厉铁帅, 等. 基于多输入模型及句法结构的中文评论情感分析方法[J]. 大数据, 2021,7(6):41-52. DOI： 10.11959/j.issn.2096-0271.2021059.

Baohua ZHANG, Huaping ZHANG, Tieshuai LI, et al. Chinese comment sentiment analysis method based on multi-input model and syntactic structure[J]. Big data research, 2021, 7(6): 41-52. DOI： 10.11959/j.issn.2096-0271.2021059.

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。

Abstract

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

Keywords

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