Exploring barriers and opportunities in industrial hemp through NLP on social media
用自然语言处理分析YouTube评论，揭示工业大麻行业在法规、市场方面的挑战，以及建筑材料和可持续纺织品等领域的机遇，并发现评论情感能预测种植面积。
Industrial hemp stakeholders face diverse and complex challenges in cultivation and processing. Platforms like YouTube have become vital spaces for farmers, processors, and supply chain managers to share insights and exchange knowledge. This study employs natural language processing (NLP) to analyze YouTube comments, revealing both the challenges and emerging opportunities in the industrial hemp sector. Our research reveals two key findings: (1) persistent frustrations regarding regulatory hurdles, particularly THC limits and policy inconsistencies, and market instability; and (2) significant optimism about hemp’s potential in construction materials, sustainable textiles, and farming technologies. By applying advanced sentiment analysis, topic modeling techniques, we identify critical concerns including production challenges, supply chain inefficiencies, and climate adaptation, while also highlighting areas of industry growth and innovation. Additionally, we developed a regression model showing that average sentiment scores strongly predict annual industrial hemp acreage planted. This study demonstrates how NLP can transform social media data into actionable insights for the hemp industry. This framework enables real-time tracking of industry sentiment and presents a flexible model for observing agricultural sectors through digital discussions. Our findings emphasize the importance of integrating data-driven approaches with traditional agricultural decision-making processes.