Title: Social media analysis – determining the number of topic clusters from buzz marketing site

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.

DOI: 10.1504/IJCSE.2012.046181

International Journal of Computational Science and Engineering, 2012 Vol.7 No.1, pp.65 - 72

Received: 08 May 2021
Accepted: 12 May 2021

Published online: 29 Mar 2012 *

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