Title: Prediction of lockdown via opinion mining from tweets using machine learning system

Authors: V. Jayalakshmi; M. Lakshmi

Addresses: Department of Computer Science, Sathyabama Institute of Science and Technology, Chennai, Tamil Nadu, 600119, India ' Department of Data Science and Business Systems, Faculty of Science and Engineering, SRM Institute of Science and Technology, Kattankulathur, 603203, Tamil Nadu, India

Abstract: Social networks are connected to the internet by architecture, facilitating instantaneous digital information sharing. Twitter users can share their thoughts and opinions. During the COVID-19 pandemic, polling and data helped choose the best health intervention. The COVID-19 pandemic showed that online forums and other electronic media spread disinformation more than the disease itself, threatening the world health system. Since December 2019, the new coronavirus has expanded significantly, infecting more Indians since March 2020.The Indian authorities locked down the country to limit citizen mobility and stop the infection. Social media outlets shaped user attitudes about the severe lockdown enforcement. We analyse user perception of lockdown enforcement by compiling lockdown 1.0, 2.0, and 3.0 tweets from many timelines. A Python tool trains and evaluates the deep learning framework using user feedback. Lockdown 3.0 and the government's policies are tested using new data after creation. Python analyses the forecast's performance in the three lockout scenarios. Simulation findings show that the proposed strategy outperforms existing classification algorithms.

Keywords: prediction; lockdown; opinion mining; tweets; machine learning; Indian government; python; COVID-19.

DOI: 10.1504/IJSSE.2025.148729

International Journal of System of Systems Engineering, 2025 Vol.15 No.4, pp.387 - 400

Received: 10 Apr 2023
Accepted: 07 Jun 2023
Published online: 22 Sep 2025 *

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