Topic Extraction from Messages in Social Computing Services: Determining the Number of Topic Clusters

Social computing services, which enable people to easily communicate and effectively share the information through the Web, are rapidly spreading recently. In such services, recognizing trend topics and analyzing their reputation from user messages have become significant. Effective topic extraction technique from messages in social computing services is needed. However, since messages contain redundancy and topic boundaries are ambiguous, it is difficult to extract appropriate topics. As a first step to extract topics, this paper proposes an effective method 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 parameter.