Hybrid Firefly-Ontology-Based Clustering Algorithm for Analyzing Tweets to Extract Causal Factors

Akilandeswari J., Jothi G., Dhanasekaran K., Kousalya K. and Sathiyamoorthi V.
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Akilandeswari J.: Department of IT, Sona College of Technology, India
Jothi G.: Department of Computer Applications, Sona College of Arts and Science, India
Dhanasekaran K.: Department of Data Science and Business Systems, School of Computing, SRM Institute of Science and Technology, Kattankulathur, India
Kousalya K.: Department of CSE, Kongu Engineering College, India
Sathiyamoorthi V.: Department of CSE, Sona College of Technology, India

International Journal on Semantic Web and Information Systems (IJSWIS), 2022, vol. 18, issue 1, 1-27

Abstract: Social media especially Twitter has become ubiquitous among people where they express their opinions on various domains. This paper presents a Hybrid Firefly – Ontology-based Clustering (FF-OC) algorithm which attempts to extract factors impacting a major public issue that is trending. In this research work, the issue of food price rise and disease which was trending during the time of the investigation is considered. The novelty of the algorithm lies in the fact that it clusters the association rules without any prior knowledge. The findings from the experimentation suggest different factors impacting the rise of price in food items and diseases such as diabetes, flu, zika virus. The empirical results show the significant improvement when compared with Artificial Bees Colony, Cuckoo Search Algorithm, Particle Swarm Optimization, and Ant Colony Optimization based clustering algorithms. The proposed method gives an improvement of 81% in terms of DB index, 79% in terms of silhouette index, 85% in terms of C index when compared to other algorithms.

Date: 2022
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