Authors: T. Hashimoto; B. Chakraborty; Y. Shirota
Addresses: Faculty of Commerce and Economics, Chiba University of Commerce, 1-3-1 Konodai, Ichikawa, Chiba, Japan. ' Faculty of Software and Information Science, Iwate Prefectural University, 152-52 Takizawa-aza-sugo, Takizawa, Iwate, Japan. ' Faculty of Economics, Gakushuin University, 1-5-1 Mejiro, Toshima-ku, Tokyo, Japan
Abstract: Social media, which enable people to easily communicate and effectively share the information through the web, are rapidly spreading recently. In such media, effective topic extraction technique from messages has been significant so that trend topics and their reputation can be recognised. However, since messages contain redundancy and topic boundaries are ambiguous, it is difficult to extract appropriate topics. As the first step for topic extraction, this paper proposes an effective measure to automatic determination of appropriate number of topics based on the intra-cluster distance and the inter-cluster distance among topic clusters. We present our experimental results to show the effectiveness of our proposed approach.
Keywords: topic clusters; social media; topic extraction; clustering; data mining; buzz marketing sites; blogs; social networking; user messages.
International Journal of Computational Science and Engineering, 2012 Vol.7 No.1, pp.65 - 72
Available online: 29 Mar 2012Full-text access for editors Access for subscribers Purchase this article Comment on this article