Tip information from social media based on topic detection

Purpose – The information of social media is not often written in ordinary web pages. Nevertheless, it is difficult to extract such information from social media because such services include so much information. Furthermore, various topics are mixed in social media communities. The authors designate such important and unique information related to social media as tip information. In this paper, they aim to propose a method to extract tip information that has been classified by topic from social networking services as a first step in extracting tip information from social media.Design/methodology/approach – Themes of many kinds exist in a social media community because users write contents freely. Then the authors first detect the topics from the community and cluster the comment based on the topics. Subsequently, they extract tip information from each cluster. In this time, the tip information is include a user's experience and it has common important words.Findings – The authors used an experiment to co...

[1]  Hong Yu,et al.  Towards Answering Opinion Questions: Separating Facts from Opinions and Identifying the Polarity of Opinion Sentences , 2003, EMNLP.

[2]  Bing Liu,et al.  Mining and summarizing customer reviews , 2004, KDD.

[3]  Satoshi Morinaga,et al.  Mining product reputations on the Web , 2002, KDD.

[4]  Mark Steyvers,et al.  Finding scientific topics , 2004, Proceedings of the National Academy of Sciences of the United States of America.

[5]  Peter D. Turney Thumbs Up or Thumbs Down? Semantic Orientation Applied to Unsupervised Classification of Reviews , 2002, ACL.

[6]  Kentaro Inui,et al.  Experience Mining: Building a Large-Scale Database of Personal Experiences and Opinions from Web Documents , 2008, 2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology.

[7]  Bing Liu,et al.  Opinion observer: analyzing and comparing opinions on the Web , 2005, WWW '05.

[8]  I-Hsien Ting,et al.  Analyzing Multi-source Social Data for Extracting and Mining Social Networks , 2009, 2009 International Conference on Computational Science and Engineering.

[9]  David M. Pennock,et al.  Mining the peanut gallery: opinion extraction and semantic classification of product reviews , 2003, WWW '03.

[10]  Yuji Matsumoto,et al.  Collecting Evaluative Expressions for Opinion Extraction , 2004, IJCNLP.

[11]  Michael I. Jordan,et al.  Latent Dirichlet Allocation , 2001, J. Mach. Learn. Res..

[12]  Bernard J. Jansen,et al.  Twitter power: Tweets as electronic word of mouth , 2009 .