Quantifying Effectiveness of Governmental Agriculture Policies Using RNN-Transformer Based Sentiment Analysis

The objective of this research paper is to understand the impact of governmental agricultural policies through a sentiment analysis approach, using advanced neural networks like RNNs and Transformer models. The study seeks to understand public sentiment on different policies and practices by analyzing emotional reactions from various stakeholders in the agricultural sector. The methodology employs farmer-targeted surveys for data collection, preprocessing of the tokens or the data, and the creation of a high-performance sentiment analysis model. This model uses RNN and Transformer techniques together to boost the predictive accuracy. The results are aimed at providing the policymakers with actionable information, thus, improving the decision-making process and the communication strategies in the agricultural community.