基于LDA主题模型的情感分析研究

doi:10.16180/j.cnki.issn1007-7820.2020.07.003

Abstract

Abstract:

LDA topic model lacks understanding of word association and related word pairs when extracting features, which affects the precision of emotional polarity classification. Aiming at this problem, this paper proposed a new model to introduce the feature-opinion pair extraction method in the LDA topic model to improve the extraction effect of the feature opinion pairs. Dependency parsing was used to design feature affective word pairs recognition methods of characteristic affective word pairs. Then the recognition method was introduced as a constraint condition into the LDA model to extract the feature sentiment word pairs. The parameters were calculated by Gibbs sampling, and the generation process of the model was proposed. Finally, the emotional polarity of the text was classified using the random forest classification method. In order to verify the validity of the proposed model, the experiment was carried out together with the other two models. When the number of subject was 20,the results showed that the precision, recall and F-Measure were 81.54%、83.13% and 82.33%, which were significantly higher than the other two models.

Key words: product reviews, sentiment analysis, dependency syntax, feature extraction, LDA topic model, random forest algorithm

CLC Number:

TP391.1

LIU Yanwen,WEI Yun. Research of Emotional Analysis Based on LDA Topic Model[J].Electronic Science and Technology, 2020, 33(7): 12-16.

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References 18

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关系类型	缩写
主谓关系	SBV
动宾关系	VOB
定中关系	ATT
动补结构	CMP
状中关系	ADV

符号	说明
α	文档-主题分布的Dirichlet超参数
β	主题-词语分布的Dirichlet超参数
θ	文档-主题概率分布
ψ	主题-词语概率分布
z	词语的主题分布
γ	依存约束相关的Dirichlet超参数
w	词语
l	情感层标签
ε	情感-主题概率分分布
v	依存约束的转换参
ψ_p	依存约束的主题词语分布
S_p	依存约束关系集
N(d,s)	文档中的词语数
K	主题数
D	文档数
S	每个文档的句子数

符号	说明
TP	在文本中为正向, 分类结果为正向的数据
FP	在文本中为负向, 分类结果为正向的数据
FN	在文本中为正向, 分类结果为负向的数据
TN	在文本中为负向, 分类结果为负向的数据

模型	LDA	JST	本文模型
抽取对数	4 023	4 361	5 125
正确对数	2 723	3 102	4 037
抽取准确率	0.676	0.711	0.787

Research of Emotional Analysis Based on LDA Topic Model

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