Clustering Model for Microblogging Sites using Dimension Reduction Techniques

Clustering Model for Microblogging Sites using Dimension Reduction Techniques

Soumi Dutta (Indian Institute of Engineering Science and Technology Shibpur, Howrah, India), Nilan Saha (Institute of Engineering & Management, Kolkata, India), Asit Kumar Das (Indian Institute of Engineering Science and Technology Shibpur, Howrah, India) and Saptarshi Ghosh (Indian Institute of Technology, Kharagpur, India)
Copyright: © 2019 |Pages: 20
DOI: 10.4018/IJISMD.2019040102

Abstract

In recent times, microblogging sites such as Twitter have become popular communication platforms for exchanging information. From the point of view of individual user, a reasonably active Twitter user can easily get hundreds of microblogs (tweets) in his/her timeline every day. In addition, a large number of the tweets contain fundamentally the same information, because of retweeting and re-posting. These huge amounts of repetitive data may cause data over-burden for the users, and no user can effectively process so much data. In this situation, methodologies to manage the data over-burden should be developed. One of the effective methods for managing the data over-burden on Twitter is to cluster semantically similar tweets into groups, with the goal that a user may see just a couple of tweets in each group. In this work, various graph clustering approaches based on dimension reduction are proposed for clustering microblogs. Through experiments on several microblog datasets, the authors demonstrate that the proposed techniques perform better than several classical text clustering algorithms.
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Clustering of microblogs is a very significant challenge in the social media analysis domain, which has resulted in some quality research works over the years. Miyamoto, Suzuki & Takumi (2012) used a fuzzy neighborhood model for analyzing the tweets, and then used two kernel-based methods along with pair wise constraints for clustering the tweets. Streamcube is a hierarchical spatiotemporal hashtag clustering system which has been proposed in Feng (2015). In their research, a spatiotemporal hierarchy inspired by quad-tree and data cube is used to perform hashtag and clustering according to a divide and conquer strategy at the most last level of the steps. Then an event ranking algorithm is used that helps users to identify local and burst events (which is one of the main problems that clustering solves). Perez-Tellez et al. proposed a method of clustering the microblog texts associated to any organization for online reputation management (Perez-Tellez, Pinto, Cardiff, & Rosso, 2011). They introduced the unsupervised TEM-Full and TEM-FULL+F methods to enrich term representation of tweets that is later used for clustering which has enabled clustering algorithms such as K-means, to obtain superior performance in clustering.

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