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

Madanjit Singh (), Amardeep Singh, Sarveshwar Bharti, Prithvipal Singh and Munish Saini
Additional contact information
Madanjit Singh: Department of Computer Science, Guru Nanak Dev University, Amritsar 143005, India
Amardeep Singh: School of Computer and Information Sciences, University of Cumberlands, Lebanon, TN 37087, USA
Sarveshwar Bharti: Department of Computer Science, Guru Nanak Dev University, Amritsar 143005, India
Prithvipal Singh: Department of Computer Science, Guru Nanak Dev University, Amritsar 143005, India
Munish Saini: Department of Computer Engineering and Technology, Guru Nanak Dev University, Amritsar 143005, India

Sustainability, 2022, vol. 14, issue 23, 1-18

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.

Keywords: agriculture; educational lessons; COVID-19; clustering; sentiment analysis; Twitter (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://www.mdpi.com/2071-1050/14/23/16174/pdf (application/pdf)
https://www.mdpi.com/2071-1050/14/23/16174/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jsusta:v:14:y:2022:i:23:p:16174-:d:992857

Access Statistics for this article

Sustainability is currently edited by Ms. Alexandra Wu

More articles in Sustainability from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().