Hybrid Clustering for Identification of Distinct Topics of a Domain using User Influence Pattern
Dwarapu Suneetha1, Mogalla Shashi2
1Dwarapu Suneetha, Assistant Professor, Department of Computer Science and Engineering, GITAM Institute of Technology, Visakhapatnam, India.
2Mogalla Shashi, Professor, Department of Computer Science and Engineering, Andhra University, Visakhapatnam, India.
Manuscript received on 01 December 2018 | Revised Manuscript received on 06 December 2018 | Manuscript Published on 26 December 2018 | PP: 62-67 | Volume-8 Issue- 2S2 December 2018 | Retrieval Number: BS2011128218/19©BEIESP
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open-access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Content based tweet clustering is extensively used for automatic topic identification of tweets in social media analytics. However due to restrictions on the length of the content in social media platforms like twitter mere content is not enough to provide sufficient information for clustering. In this paper the authors proposed to enhance the clustering quality by adding tweeting behavior of influential users. Spearmen correlation is appropriately adapted for identifying mergeable clusters. A new methodology for hybrid clustering is proposed and tested using entropy on real data related to three domains namely sports, politics, and health. The proposed method achieved distinct cluster formation which is reflected by reduced entropy after applying merging based on user influence patterns.rays, obtained from literatures has been presented.
Keywords: Tweet Clustering, Entropy, Influence Patterns, Hybrid Clustering.
Scope of the Article: Computer Science and Its Applications