Block chain-Based Sentiment Analysis of Agriculture News Headlines

Abstract

Agriculture sector plays a very important role in ensuring food security of all human being and economic development across the world in each and every country around the world. With the fast and exponential growth of digital media, large volumes of agriculture related news headlines are published daily through online news portals, blogs and social media platforms. These headlines significantly influence and may impact farmers, policymakers, investors, and agricultural markets. Extracting insights and information from such huge textual data manually is a difficult and time-consuming task. Textual data can be automatically categorized into neutral, negative or positive sentiments using sentiment analysis, a feature of NLP ( Natural language processing). This study presents a framework to perform a sentiment analysis on agricultural news headlines using a machine learning approach.Later on Block chain technology is integrated with it by which it ensures secure, transparent, and tamper-proof storage of analyzed data. The proposed model combines sentiment analysis with Block chain-based data management to improve transparency, reliability and accessibility of agricultural information. The proposed system serves as a valuable tool for stakeholders in the agricultural sector by enabling analysis of agricultural trends, market dynamics, policy impacts, crop planning, yield forecasting, risk mitigation strategies, price prediction, and supply chain management