User Segmentation Based on Twitter Data Using Fuzzy Clustering

Social Networking Sites, which create platform for social interactions and sharing are the mostly used internet websites, thus are very important in today’s world. The vast usage of social networking sites (SNSs) has effected the business world, new business models are proposed, business process are renewed and companies try to create benefit form these sites. Besides the functional usage of SNSs such as marketing and customer relations, companies can create value by analyzing and mining the data on SNSs. In this paper, a new segmentation approach, using Text Mining and Fuzzy Clustering techniques. Text mining is process of extracting knowledge from large amounts of unstructured data source such as content generated by the SNSs users. Fuzzy clustering is an algorithm for cluster analysis in which the allocation of data points to clusters is fuzzy. In the proposed approach, users self description text are used as an input to the Text Mining process, and Fuzzy Clustering is used to extract knowledge from data. Using the proposed approach, companies can segment their customers based on their comments, ideas or any kind of other unstructered data on SNSs. DOI: 10.4018/978-1-4666-4213-3.ch014