Predictive Analytical Model for Microblogging Data Using Asset Bubble Modelling

Srinidhi Hiriyannaiah, Siddesh G.M. and Srinivasa K.G.
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Srinidhi Hiriyannaiah: Department of ISE, Ramaiah Institute of Technology (MSRIT), Bangalore-560054, India & Visvesvaraya Technological University, Belagavi, Karnataka, India
Siddesh G.M.: Department of ISE, Ramaiah Institute of Technology (MSRIT), Bangalore-560054, India & Visvesvaraya Technological University, Belagavi, Karnataka, India
Srinivasa K.G.: National Institute of Technical Teachers Training and Research, Chandigarh, India

International Journal of Cognitive Informatics and Natural Intelligence (IJCINI), 2020, vol. 14, issue 2, 108-118

Abstract: In recent days, social media plays a significant role in the ecosystem of the big data world and its different types of information. There is an emerging need for collection, monitoring, analyzing, and visualizing the different information from various social media platforms in different domains like businesses, public administration, and others. Social media acts as the representative with numerous microblogs for analytics. Predictive analytics of such microblogs provides insights into various aspects of the real-world entities. In this article, a predictive model is proposed using the tweets generated on Twitter social media. The proposed model calculates the potential of a topic in the tweets for the prediction purposes. The experiments were conducted on tweets of the regional election in India and the results are better than the existing systems. In the future, the model can be extended for analysis of information diffusion in heterogeneous systems.

Date: 2020
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