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.

Clustering Algorithms in Sentiment Analysis Techniques in Social Media – A Rapid Literature Review

“…Therefore, for each paper in the database, the sector of activity was extracted by Monkeylearn from Title and Abstract variables, using a pretrained model. Several studies approach different hybrid clustering algorithms [47], [51], [68] while others develop new algorithms [36] for the purpose of grouping twits on similar topics related to COVID-19. Consequently, in order to understand city-level differences in emotions regarding COVID-19 vaccine-related subjects in the three biggest South African cities, [47] employ clustered geo-tagged Twitter postings.…”

“…The authors [68] employ Uniform Manifold Approximation and Projection (UMAP) and Hierarchical Density-Based Spatial Clustering of Ap-plications with Noise (HDBSCAN). They combine the techniques to undertake a grid search for identifying the clustering model with the highest relative validity score representative for COVID-19.…”

Evaluating Social Media Marketing in the Greek Winery Industry

“…In the literature, these tools have been used in different sectors, such as agriculture, tourism [30], politics, health, and business [40]. Specifically for the agricultural sector, there are examples of the use of SMA tools to generate knowledge about plant diseases from posts made by a community of farmers within SM [41]; the behavior of agricultural stakeholders during COVID-19 through agriculture-related tweets [42]; and farmers' attitudes towards technology adoption [43].…”