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

Baohua ZHANG; Huaping ZHANG; Tieshuai LI; Jianyun SHANG

doi:10.11959/j.issn.2096-0271.2021059

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

TOPIC: BIG DATA-ENABLED INTELLIGENT APPLICATIONS | 更新时间：2024-06-03

- Chinese comment sentiment analysis method based on multi-input model and syntactic structure
- Big data research Vol. 7, Issue 6, Pages: 41-52(2021)
- 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：
- 基金信息：
- DOI：10.11959/j.issn.2096-0271.2021059
  CLC： TP183
- Online First：2021-11，
  
  Published：15 November 2021
- 稿件说明：
移动端阅览
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：

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

references

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