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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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Abstract [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.
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Received: 25 January 2016
Published: 29 September 2016
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