Analyzing tweet cluster using standard fuzzy C means clustering

Abstract

Since the inception of Web 2.0. the effort of socializing, interacting and referencing has been substantially enhanced.This is completely aided through the various means of social network expansions like blogging, public chat rooms and social networking sites such as Facebook, Twitter etc. Behavior on these websites leaves an electronic trail of social activity which can be analyzed and valuable information can be discerned. The development of such analysis has become phenomenal to foster psychological analysis, behavioral modeling and even commercializing the business activities under those paradigms itself. Therefore, micro-blogging service Tweeter recently has gained much interest to social network community with the trend of its Follower/Following Relationship, Mentions, trends, retweet, Twitter Lists etc. and the result of such impact could be realized while investigating diversified tweet clusters under the same community and under the same relevant discussion of topic. This chapter initiates a novel idea to analyze the random tweet cluster and its relevant trend through computational intelligence e.g. through Standard Fuzzy C Means clustering. The idea solicits and introduces a better method of clustering with more number of actually found dynamic clusters. Results have been evaluated with broader implication of analysis and research in futuristic Tweeter network. © 2014 Springer International Publishing Switzerland.