Start Date
17-10-2025 9:30 AM
End Date
17-10-2025 10:00 AM
Submission Type
Abstract
Track
Agribusiness
Abstract
This study applies sentiment analysis to public commentary on U.S. farm bills to assess patterns of support and opposition. Using Reddit posts from relevant subreddits, we employed two natural language processing models, VADER and RoBERTa, to evaluate sentiment over time. While both models yielded differing sentiment distributions, they revealed increased public engagement during farm bill years, particularly in 2018. Most sentiments were neutral, but negative opinions outweighed positive ones. Posts with stronger sentiments tended to attract more user interaction, especially controversial ones. These findings highlight the polarized yet active public discourse surrounding agricultural policy and the potential for data-driven policy insight.
Included in
Agribusiness Commons, Business Analytics Commons, Business Intelligence Commons, Management Sciences and Quantitative Methods Commons
Sentiment Analysis of Public Commentary on U.S. Farm Bills
This study applies sentiment analysis to public commentary on U.S. farm bills to assess patterns of support and opposition. Using Reddit posts from relevant subreddits, we employed two natural language processing models, VADER and RoBERTa, to evaluate sentiment over time. While both models yielded differing sentiment distributions, they revealed increased public engagement during farm bill years, particularly in 2018. Most sentiments were neutral, but negative opinions outweighed positive ones. Posts with stronger sentiments tended to attract more user interaction, especially controversial ones. These findings highlight the polarized yet active public discourse surrounding agricultural policy and the potential for data-driven policy insight.
