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

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

Akilandeswari J., Jothi G., Dhanasekaran K., Kousalya K., Sathiyamoorthi V.
Copyright: © 2022 |Volume: 18 |Issue: 1 |Pages: 27
ISSN: 1552-6283|EISSN: 1552-6291|EISBN13: 9781799893967|DOI: 10.4018/IJSWIS.295550
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MLA

Akilandeswari J., et al. "Hybrid Firefly-Ontology-Based Clustering Algorithm for Analyzing Tweets to Extract Causal Factors." IJSWIS vol.18, no.1 2022: pp.1-27. http://doi.org/10.4018/IJSWIS.295550

APA

Akilandeswari J., Jothi G., Dhanasekaran K., Kousalya K., & Sathiyamoorthi V. (2022). Hybrid Firefly-Ontology-Based Clustering Algorithm for Analyzing Tweets to Extract Causal Factors. International Journal on Semantic Web and Information Systems (IJSWIS), 18(1), 1-27. http://doi.org/10.4018/IJSWIS.295550

Chicago

Akilandeswari J., et al. "Hybrid Firefly-Ontology-Based Clustering Algorithm for Analyzing Tweets to Extract Causal Factors," International Journal on Semantic Web and Information Systems (IJSWIS) 18, no.1: 1-27. http://doi.org/10.4018/IJSWIS.295550

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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.