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数据分析与知识发现  2019, Vol. 3 Issue (2): 98-107     https://doi.org/10.11925/infotech.2096-3467.2018.0578
  研究论文 本期目录 | 过刊浏览 | 高级检索 |
基于中文社交媒体文本的领域情感词典构建方法研究*
蒋翠清1,2(),郭轶博1,刘尧1
1合肥工业大学管理学院 合肥 230009
2过程优化与智能决策教育部重点实验室 合肥 230009
Constructing a Domain Sentiment Lexicon Based on Chinese Social Media Text
Cuiqing Jiang1,2(),Yibo Guo1,Yao Liu1
1School of Management, Hefei University of Technology, Hefei 230009, China
2Key Laboratory of Process Optimization and Intelligent Decision-making of Ministry of Education, Hefei 230009, China
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摘要 

【目的】从社交媒体用户生成内容中发现未知情感词, 构造领域情感词典, 应用于汽车评论的情感分析。【方法】选取HowNet情感词典作为种子, 以实际汽车评论作为语料, 分别利用PMI和Word2Vec算法识别新词情感极性, 根据集成规则对二者识别结果综合判定, 通过情感分类实验对比显示本文算法的有效性。【结果】按照该方法构造的情感词典准确率比HowNet情感词典提高21.6%, 较分别使用PMI和Word2Vec算法构建的词典分别提升3.7%和2.1%, 同时正面、负面情感词数量均有大幅增加。【局限】语料来源单一, 应用于其他领域具有一定局限性。【结论】该方法构造的情感词典可有效应用于社交媒体文本情感分析。

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蒋翠清
郭轶博
刘尧
关键词 社交媒体情感分析情感词典PMIWord2Vec    
Abstract

[Objective] This study aims to construct a domain sentiment lexicon by discovering unrecognized sentiment words from user-generated contents on Chinese social media to apply it to automotive comments sentiment analysis. [Methods] First, words in HowNet are selected as the seeds, and PMI and Word2Vec algorithm are used to calculate the sentiment polarity of the candidates respectively on real automative corpus. Then the results of the two discriminations are judged synthetically according to the ensemble rules. Finally the proposed method was shown effective by the comparison of the sentiment classification experiments. [Results] The accuracy rate of the lexicon constructed according to proposed method is 21.6% higher than that of HowNet. The lexicon constructed by PMI and Word2Vec respectively increase 3.7% and 2.1%. Meanwhile the number of positive and negative emotional words are greatly increased. [Limitations] The source of corpus is single, and it has certain limitations in guiding other fields. [Conclusions] The sentiment lexicon constructed by this method can be applied to sentiment analysis of social media texts effectively.

Key wordsSocial Media    Sentiment Analysis    Sentiment Lexicon    PMI    Word2Vec
收稿日期: 2018-05-23      出版日期: 2019-03-27
基金资助:*本文系国家自然科学基金项目“基于社交媒体用户生成内容的产品创新需求发现方法研究”(项目编号: 71571059)、国家自然科学基金重点项目“大数据环境下的微观信用评价理论与方法研究”(项目编号: 71731005)和安徽省教育厅高校人文社会科学重大研究项目“社会媒体环境下企业舆情演化机理与管控研究”(项目编号: SK2014ZD054)的研究成果之一
引用本文:   
蒋翠清,郭轶博,刘尧. 基于中文社交媒体文本的领域情感词典构建方法研究*[J]. 数据分析与知识发现, 2019, 3(2): 98-107.
Cuiqing Jiang,Yibo Guo,Yao Liu. Constructing a Domain Sentiment Lexicon Based on Chinese Social Media Text. Data Analysis and Knowledge Discovery, 2019, 3(2): 98-107.
链接本文:  
https://manu44.magtech.com.cn/Jwk_infotech_wk3/CN/10.11925/infotech.2096-3467.2018.0578      或      https://manu44.magtech.com.cn/Jwk_infotech_wk3/CN/Y2019/V3/I2/98
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