Paper Info
Title
Topic Extraction from Messages in Social Computing Services: Determining the Number of Topic Clusters
Abstract
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.
Year
DOI
Venue
2010
10.1109/ICSC.2010.70
ICSC
Keywords
Field
DocType
Internet,data mining,redundancy,social networking (online),World Wide Web,intercluster-distance,intracluster distance,redundancy,social computing services,topic boundary,topic clusters,topic extraction,Clustering,Data Mining,Number of Topics,Social Computing Services
Cluster (physics),World Wide Web,Computer science,Effective method,Visualization,Redundancy (engineering),Cluster analysis,Social computing,Reputation,The Internet
Conference
ISSN
Citations 
PageRank 
2325-6516
2
0.39
References 
Authors
0
2
Name
Order
Citations
PageRank
Basabi Chakraborty110923.21
Takako Hashimoto25018.47