Outline
- 01Summary
- 02How the tool works
- 03The latest version
- 04The previous version
- 05Partner
While the impact of rising food prices may be clear, the root causes of such price increases often are not. The role of various short- and long-term factors, such as the growth of commodities futures markets and changing levels of grain stocks, continues to be debated. This tool generates up-to-date media daily analysis of factors that may influence commodity price volatility and food security.
Last update: July 2026 (Daily)
The Food Price Media Analysis is an advanced Agentic AI system developed with the Google Gemini Large Language Model, designed to track public anxiety and market sentiments by autonomously scraping and analyzing daily global news. This agent structures and displays visual sentiment trends across critical food commodities (such as wheat, maize, soybean, and rice) as well as energy prices, allowing users to understand public concern in real time. It serves as a digital public good, providing vital indicators through the public Food Security Media Analysis System dashboard to aid analysts, researchers, and policymakers in navigating potential market shocks. By leveraging advanced Large Language Model reasoning, this approach excels at understanding complex narrative context and implicit market sentiment across lengthy articles, bypassing the rigid limitations and high false-positive rates of traditional Natural Language Processing techniques. The previous version of this tool was developed in collaboration with GATE group from the University of Sheffield to analyze media coverage of food security, food prices, and hunger.
How the tool works
The Food Security Media Analysis System has evolved from its original architecture, which relied on the General Architecture for Text Engineering (GATE) suite to employ advanced linguistic and semantic object network-mapping algorithms for tracking term relationships. While that previous system mined the daily, global news corpus by using rigid rules to identify sentences mentioning price movements, the system now utilizes the Google Gemini Large Language Model to achieve a much deeper understanding of context. By transitioning to this advanced Large Language Model reasoning, the system excels at interpreting complex narrative context and implicit market sentiment across lengthy journalistic articles, bypassing the structural limitations and high false-positive rates of those traditional, pattern-reliant text-mining techniques.
The latest version
The previous version
This version was maintained until May 2026
Partner
