结合语言知识和深度学习的中文文本情感分析方法

徐康庭; 宋威

doi:10.11959/j.issn.2096-0271.2022026

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结合语言知识和深度学习的中文文本情感分析方法

研究 | 更新时间：2024-06-03

- 结合语言知识和深度学习的中文文本情感分析方法
- A Chinese text sentiment analysis method combining language knowledge and deep learning
- 大数据 2022年8卷第3期页码：115-127
- 作者机构：
- 作者简介：
  
  [ "徐康庭（2000- ），男，北方工业大学信息学院在读，主要研究方向为机器学习、自然语言处理。" ]
  [ "宋威（1980- ），男，博士，北方工业大学信息学院教授、博士生导师，主要研究方向为数据挖掘、推荐系统。" ]
- 基金信息：
  
  国家自然科学基金资助项目;The National Natural Science Foundation of China(61977001)
- DOI：10.11959/j.issn.2096-0271.2022026
  中图分类号： TP391.1
- 网络首发：2022-05，
  
  纸质出版：2022-05-15
- 稿件说明：
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徐康庭, 宋威. 结合语言知识和深度学习的中文文本情感分析方法[J]. 大数据, 2022,8(3):115-127.

Kangting XU, Wei Song. A Chinese text sentiment analysis method combining language knowledge and deep learning[J]. Big data research, 2022, 8(3): 115-127.
徐康庭, 宋威. 结合语言知识和深度学习的中文文本情感分析方法[J]. 大数据, 2022,8(3):115-127. DOI： 10.11959/j.issn.2096-0271.2022026.

Kangting XU, Wei Song. A Chinese text sentiment analysis method combining language knowledge and deep learning[J]. Big data research, 2022, 8(3): 115-127. DOI： 10.11959/j.issn.2096-0271.2022026.

摘要

在目前的中文文本情感分析研究中，基于语义规则和情感词典的方法通常需要人工设置情感阈值；而基于深度学习的方法由于未能运用语义规则和情感词典等语言知识，不能充分提取情感特征。针对这两种方法的缺点，提出了一种将语言知识和深度学习结合的文本情感分析方法。该方法首先根据语义规则提取文本中的关键情感片段，再根据情感词典从关键情感片段中抽取出情感更加明确的情感词来构建情感集合，然后利用深度学习模型分别从原始文本、关键情感片段、情感集合中抽取深层次特征，最后对提取的特征进行加权融合，并利用分类器实现情感极性的判断。实验结果表明，与未引入语言知识的深度学习模型相比，该方法的情感极性分类能力有明显提升。

Abstract

At present

in the research of Chinese text emotion analysis

the method based on semantic rules and emotion dictionary usually needs to set the emotional threshold manually.However

the method based on deep learning can’t fully extract emotional features because it fails to use language knowledge such as semantic rules and emotional dictionary.As to shortcomings of two methods

a text emotion analysis method combining language knowledge and deep learning was proposed.Firstly

the key emotional segments in the text were extracted according to the semantic rules.Secondly

more explicit emotion words were extracted from the key emotional segments according to the emotional dictionary to construct the emotion set.Thirdly

the deep level features were extracted from the original text

key emotional segments and emotional set by using the deep learning model.Finally

the features were weighted and fused

and the classifier was used to judge the emotional polarity.The experimental results show that compared with the deep learning model without language knowledge

this method has significantly improved the ability of emotional polarity classification.

关键词

Keywords

references

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