Integrated microblog sentiment analysis from users' social interaction patterns and textual opinions

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• 作者：Yau-Hwang Kuo ; Meng-Hsuan Fu ; Wen-Hao Tsai ; Kuan-Rong Lee…
• 关键词：Opinion mining ; Sentiment analysis ; Microblog ; Social network ; Relaxation labeling
• 刊名：Applied Intelligence
• 出版年：2016
• 出版时间：March 2016
• 年：2016
• 卷：44
• 期：2
• 页码：399-413
• 全文大小：6,146 KB
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• 作者单位：Yau-Hwang Kuo (1) (2)
  Meng-Hsuan Fu (1)
  Wen-Hao Tsai (1)
  Kuan-Rong Lee (3)
  Ling-Yu Chen (1)
  
  1. Center for Research of E-life Digital Technology, Department of Computer Science and Information Engineering, National Cheng Kung University, Tainan, Taiwan
  2. Department of Computer Science, National Chengchi University, Taipei, Taiwan
  3. Department of Computer Science and Information Engineering, Kun Shan University, Tainan, Taiwan
  
• 刊物类别：Computer Science
• 刊物主题：Artificial Intelligence and Robotics
  Mechanical Engineering
  Manufacturing, Machines and Tools
  
• 出版者：Springer Netherlands
• ISSN：1573-7497

文摘

Traditional post-level opinion classification methods usually fail to capture a person’s overall sentiment orientation toward a topic from his/her microblog posts published for a variety of themes related to that topic. One reason for this is that the sentiments connoted in the textual expressions of microblog posts are often obscure. Moreover, a person’s opinions are often influenced by his/her social network. This study therefore proposes a new method based on integrated information of microblog users’ social interactions and textual opinions to infer the sentiment orientation of a user or the whole group regarding a hot topic. A Social Opinion Graph (SOG) is first constructed as the data model for sentiment analysis of a group of microblog users who share opinions on a topic. This represents their social interactions and opinions. The training phase then uses the SOGs of training sets to construct Sentiment Guiding Matrix (SGM), representing the knowledge about the correlation between users’ sentiments, Textual Sentiment Classifier (TSC), and emotion homophily coefficients of the influence of various types of social interaction on users’ mutual sentiments. All of these support a high-performance social sentiment analysis procedure based on the relaxation labeling scheme. The experimental results show that the proposed method has better sentiment classification accuracy than the textual classification and other integrated classification methods. In addition, IMSA can reduce pre-annotation overheads and the influence from sampling deviation.