Predictive Analytical Model for Microblogging Data Using Asset Bubble Modelling

Predictive Analytical Model for Microblogging Data Using Asset Bubble Modelling

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) and Srinivasa K.G. (National Institute of Technical Teachers Training and Research, Chandigarh, India)
DOI: 10.4018/IJCINI.2020040107

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.
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Social media platforms allow collecting data and analyzing different types of information from different users and on different topics. Examples of the social media platforms include Facebook, Google+, Twitter and others. Facebook and other social media platforms data need to be statically collected and analyzed whereas twitter provides a streaming API wherein the data can be gathered in real-time. Hence, in this paper the proposed method is used with microblog twitter. Many of the experiments related to sentimental analysis choose twitter as the source since it provides the streaming service for analysis (Owen et.al). However, there are certain drawbacks of the streaming service that include scalability and processing. In order to address this issue, the proposed system includes Apache kafka as the platform for receiving the messages from the twitter. Apache kafka includes a message queuing system wherein it can collect one million messages per second (Kreps et al., 2011).

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