| 情感分析研究的知识结构及热点前沿探析 |
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| 引用本文： | 周建,刘炎宝,刘佳佳.情感分析研究的知识结构及热点前沿探析[J].情报学报,2020,39(1):111-124. |
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| 作者姓名： | 周建 刘炎宝 刘佳佳 |
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| 作者单位： | 上海大学管理学院,上海 200444;上海大学管理学院,上海 200444;上海大学管理学院,上海 200444 |
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| 基金项目： | 国家自然科学基金项目“领先用户视角下基于数据驱动的质量创新方法研究”（71872110） |
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| 摘 要： | 为了解国内外情感分析领域的研究状况,揭示该领域的知识结构、研究热点与发展动态,本文采用共被引分析、聚类分析、共词分析、战略坐标分析等方法,借助CiteSpace、UCINET、BICOMB、SPSS等软件,对Web of Science数据库收录的以情感分析为主题的相关文献进行计量分析与知识图谱绘制。分析结果表明,情感分析的应用、深度学习与神经网络、电子商务下的产品评论、事物情感特征评分、社交网络下用户生成内容、语义定向广告技术以及文本语言属性分析构建了情感分析的知识结构,产品评论与口碑、数据挖掘与人工智能、无监督学习、HadoopMapReduce与支持向量机以及神经网络与深度学习为该领域的研究热点,而顾客评论、推荐系统、极性分类、主题模型、电影评论、推特数据将是未来该领域主要研究方向。
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| 关 键 词： | 情感分析 文献计量学 引文分析 共词分析 聚类分析 |
Exploration of Intellectual Structure and Hot Issues in Sentiment Analysis Research |
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| Zhou Jian,Liu Yanbao,Liu Jiajia.Exploration of Intellectual Structure and Hot Issues in Sentiment Analysis Research[J].Journal of the China Society for Scientific andTechnical Information,2020,39(1):111-124. |
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| Authors: | Zhou Jian Liu Yanbao Liu Jiajia |
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| Institution: | (School of Management,Shanghai University,Shanghai 200444) |
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| Abstract: | To comprehensively and systematically understand the development of affective analysis, this paper conducts bibliometric analysis and knowledge map analysis of affective analysis-themed literature included in the Web of Science with the help of software such as CiteSpace, UCINET, BICOMB, and SPSS. Using co-citation analysis, cluster analysis,co-word analysis, strategic coordinate analysis, and other methods, the knowledge structure, research foci, and trends in this field are revealed as follows:(i) The application of affective analysis, deep learning, and neural networks, product reviews of e-commerce, marking of emotional characteristics of things, user-generated content under social networks, semantic-oriented advertising technology, and textual language attribute analysis constitutes the knowledge structure of affective analysis.(ii) The researchers in this field focus on product comments and reputation, data mining and artificial intelligence,unsupervised learning, Hadoop-MapReduce and support vector machines, and neural networks and deep learning.(iii) Future research mainly includes customer reviews, recommendation systems, polarity classifications, subject models, movie reviews, and Twitter data. |
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| Keywords: | sentiment analysis bibliometrics co-citation analysis co-word analysis cluster analysis |
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