评论簇在网络舆论中的情感倾向代表性研究  

Gauging Public Opinion with Comment-Clusters

作　　者：杨小平[1] 马奇凤 余力[1] 莫雨婷 吴佳楠[1] 张悦[1] 

机构地区：[1]中国人民大学信息学院,北京100872

出　　处：《现代图书情报技术》2016年第7期51-59,共9页New Technology of Library and Information Service

基　　金：北京市教育委员会科技计划面上项目"文本挖掘若干关键问题的研究"(项目编号:KM201511232016);国家自然科学基金项目"推荐–采纳模式下的病毒营销用户影响研究"(项目编号:71271209)的研究成果之一

摘　　要：【目的】验证评论簇在网络舆论中具有情感倾向代表性作用。【方法】提出基于社会网络分析的评论簇对象情感倾向性分析模型。针对网络新闻事件,以新闻的用户评论(评论总集)为语料数据,对语料数据进行结构化处理和分析,借助评论主体的形式化关系,建立具有网络节点和拓扑连接关系的知识图谱,寻找最优评论簇。以评论簇的评论主体及其对应的评论对象为主,对评论簇内的核心人物及其评论进行语义分析,计算得到评论簇情感倾向,并与对应新闻的评论总集情感倾向作对比。【结果】实验结果表明,评论簇和评论总集中的情感强度趋于一致,新闻的评论簇对新闻具有较好的情感倾向代表性,并能将网络舆情对象情感挖掘算法的性能提高58%。【局限】由于本文的评论簇对象情感倾向性分析模型在情感特征词识别和抽取方法上使用不够完善,导致少量中文分词和词性标注错误、语法依存关系错误,且未将程度词考虑在内。【结论】评论簇在网络舆论中具有情感倾向代表性作用,可提高网络舆情对象情感计算的性能,可灵活有效地降低舆情分析的时间和空间复杂度。[Objective] The paper examines the role of comment-clusters in public opinion mining. [Methods] We proposed a model to study the Comment-Clusters based on social network analysis techniques. First, we collected comments received by online news reports on three trending events as raw data. Second, we analysed structures and contents of these comments with the help of the vector relationship among them to identify the best comment-clusters. Finally, we conducted semantic analysis of the key players and their comments to investigate their sentiments and then compared them with those of the whole data set. [Results] The sentiments got from the whole data set and the comment-clusters were very close to each other. Comment-Clusters improved the performance of public opinion mining algorithm. [Limitations] The method of identifying and extracting sentiment words might yield errors. [Conclusions] The comment-clusters improve the sentiment orientation computing, which helps us obtain the public opinion more efficiently.

关 键 词：语义网络 知识图谱 核心人物 网络舆情 评论簇 情感倾向计算 

分 类 号：TP391.1[自动化与计算机技术—计算机应用技术]