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Analyzing News Articles about Food Banks

This summer, we wanted to use machine learning to analyze food insecurity caused by the economic fallout of the coronavirus pandemic. Our first idea was to analyze Google Search Trends about food bank-related search terms. We wanted to see if the geographic distribution of trending searches would reveal where food banks were being overwhelmed, but ultimately the publicly-available data wasn’t good enough to do something like that. We then decided to analyze news articles about food banks instead, and glean the trends from analyzing articles at scale. We began with an initial set of over a million English news articles from the AYLIEN news tracking service. Then we filtered them to articles mentioning food banks, and related terms. We then applied topic modelling, sentiment analysis, and (zero-shot) topic classification to analyze the articles. Topic Modelling (Latent Dirichlet Allocation) Topic modelling is the p