[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.
杨小平,马奇凤,余力,莫雨婷,吴佳楠,张悦. 评论簇在网络舆论中的情感倾向代表性研究*[J]. 现代图书情报技术, 2016, 32(7-8): 51-59.
Yang Xiaoping,Ma Qifeng,Yu Li,Mo Yuting,Wu Jia’nan,Zhang Yue. Gauging Public Opinion with Comment-Clusters. New Technology of Library and Information Service, DOI：10.11925/infotech.1003-3513.2016.07.07.
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