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

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 major public issue that is trending. In this research work, the of food price rise and disease was trending during time investigation considered. The novelty lies in fact it clusters association rules without any prior knowledge. findings from experimentation suggest different items diseases such as diabetes, flu, zika virus. empirical results show significant improvement when compared with Artificial Bees Colony, Cuckoo Search Algorithm, Particle Swarm Optimization, Ant Colony Optimization based clustering algorithms. proposed method gives an 81% terms DB index, 79% silhouette 85% C index other