Social media analysis – determining the number of topic clusters from buzz marketing site. (1st January 2012)

• Record Type: Journal Article
• Authors: Hashimoto, T.; Chakraborty, B.; Shirota, Y.
• Is Part Of: International journal of computational science and engineering. Volume 7:Number 1(2012)
• Page Start: 65
• Page End: 72
• Publication Date: 2012-01-01
• Subjects: number of topic clusters -- social media -- topic extraction -- clustering -- data mining; Computer science -- Mathematics -- Periodicals; Computer simulation -- Mathematical aspects -- Periodicals; Computational intelligence -- Periodicals; 004.015105
• Languages: English
• ISSNs: 1742-7185

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.