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New Technology of Library and Information Service  2016, Vol. 32 Issue (7-8): 51-59    DOI: 10.11925/infotech.1003-3513.2016.07.07
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Gauging Public Opinion with Comment-Clusters
Yang Xiaoping,Ma Qifeng,Yu Li(),Mo Yuting,Wu Jia’nan,Zhang Yue
School of Information, Renmin University of China, Beijing 100872, China
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[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.

Key wordsSemantic network      Knowledge mapping      Key person      Web public opinion      Comment-Clusters      Sentiment orientation computing     
Received: 25 January 2016      Published: 29 September 2016

Cite this article:

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, 2016, 32(7-8): 51-59.

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