Harnessing big data analytics for agricultural extension: Applications of automated data extraction and sentiment analysis

Abstract

The integration of big data analytics in agricultural extension offers a transformative approach to improving research, decision-making and outreach. This paper explores two key methodologies—automated data extraction and sentiment analysis—demonstrating their applications in agricultural extension. Using Krishikosh, an online repository of agricultural theses, automated data extraction identified research trends in agricultural extension, revealing key topics such as adoption and knowledge transfer. Sentiment analysis, applied to Twitter data on the topic of cow slaughter during Eid, provided insights into public opinion, highlighting a predominantly negative sentiment. By employing advanced tools such as R software, both techniques efficiently processed large datasets, enabling more precise, data-driven insights. Despite challenges related to data access, privacy, and technical expertise, the study emphasizes the potential of big data analytics to enhance agricultural productivity, sustainability, and responsiveness. This paper serves as a practical guide for researchers and extension professionals to harness big data for more informed decision-making and tailored outreach strategies.