Informational Flow on Twitter – Corona Virus Outbreak – Topic Modelling Approach

Prabhakar Kaila, Dr. Rajesh; Prasad, Dr. A. V. Krishna

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Informational Flow on Twitter – Corona Virus Outbreak – Topic Modelling Approach

International Journal of Advanced Research in Engineering and Technology (IJARET), 11 (3), 2020, pp 128-134.

7 Pages Posted: 31 Mar 2020

See all articles by Dr. Rajesh Prabhakar Kaila

Dr. Rajesh Prabhakar Kaila

Visiting Professor (Big Data Analytics), NMIMS Hyderabad, NMIMS University, India

Dr. A. V. Krishna Prasad

Associate Professor, MVSR Engineering College, Hyderabad, India

Date Written: March 31, 2020

Abstract

The study focuses on the information flow on twitter during the corona virus outbreak. Tweets related to #coronavirus are studied using sentiment analysis and topic modelling using Latent Dirichlet Allocation post preprocessing. The study concluded that the information flow was accurate and reliable related to corona virus outbreak with minimum misinformation. LDA analysis had identified the most relevant and accurate topics related to corona virus outbreak and sentiment analysis confirmed the prevalence of negative sentiments like fear along with positive sentiments like trust. Governments and Healthcare authorities & institutions effectively utilized to spread accurate and reliable information on twitter.

Keywords: Data Analytics, Text Mining, Topic Modelling, Latent Dirichlet Allocation, Sentiment Analysis, Unsupervised Machine Learning

Suggested Citation: Suggested Citation

Prabhakar Kaila, Dr. Rajesh and Prasad, Dr. A. V. Krishna, Informational Flow on Twitter – Corona Virus Outbreak – Topic Modelling Approach (March 31, 2020). International Journal of Advanced Research in Engineering and Technology (IJARET), 11 (3), 2020, pp 128-134., Available at SSRN: https://ssrn.com/abstract=3565169