A text analytics model for agricultural knowledge discovery and sustainable food production: A case study from Oklahoma Panhandle

Abstract

With recent increases in the use of social media in agricultural communities, many farmers are showing more and more interest in participating in social media and sharing different aspects of their profession with peers and policymakers. However, researchers have not explored this valuable data source well enough to help improve agribusiness decision-making. This study aims to investigate the potential capability and richness of social media for agricultural knowledge discovery, which can help monitor, detect, and predict critical agricultural events and activities and develop more sustainable food production and agricultural economy. This research utilizes text-mining tools and techniques to collect, process, and mine unstructured textual data from Twitter. Then, it examines the agreement between the retrieved information from tweets and that from the current monitoring systems and common cultural practices. Our findings illustrate that social media data can effectively provide information regarding the commencement and duration of significant cultural activities This study also examines the classification performance of several popular sentiment analysis tools on the farmer’s tweets and provides suggestions for future research on domain-specific sentiment lexicons for agricultural purposes.