Using Social Media Analytics and Machine Learning Approaches to Analyze the Behavioral Response of Agriculture Stakeholders during the COVID-19 Pandemic

Abstract

COVID-19, over time, has spread around multiple countries and has affected a large number of humans. It has influenced diverse people’s lives, consisting of social, behavioral, physical, mental, and economic aspects. In this study, we aim to analyze one such social impact: the behavioral aspects of agriculture stakeholders during the pandemic period in the Indian region. For this purpose, we have gathered agriculture-related tweets from Twitter in three phases: (a) initial phase, (b) mid-phase, and (c) later phase, where these phases are related to the period of complete lockdown implemented in India in the year 2020. Afterward, we applied machine-learning-based qualitative-content-based methods to analyze the sentiments, emotions, and views of these people. The outcomes depicted the presence of highly negative emotions in the initial phase of the lockdown, which signifies fear of insecurity among the agriculture stakeholders. However, a decline in unhappiness was noted during the later phase of the lockdown. Furthermore, these outcomes will help policymakers to obtain insights into the behavioral responses of agricultural stakeholders. They can initiate primitive and preventive actions accordingly, to tackle such issues in the future.