In the era of mobile social networks, sentiment analysis has become a crucial tool for understanding public opinion across various domains, including agriculture. This study presents an advanced bidirectional encoder representations from transformers (BERT) model designed to analyse sentiment trends within the agricultural market, supported by machine learning (ML), deep learning (DL), and internet of things (IoT) technologies. By evaluating post-purchase reviews and textual data categorised as positive, negative, or neutral, the model captures valuable insights into consumer perceptions and emotional responses. These findings assist farmers, buyers, and producers in improving product quality and market strategies. Additionally, the study assesses agricultural productivity and performance metrics using the BERT framework, demonstrating its superiority over existing ML and DL models in sentiment classification accuracy and reliability.
Setty et al. (Thu,) studied this question.