mattmurray/topic_modelling_financial_news
Topic modelling on financial news with Natural Language Processing
This project helps financial analysts and researchers understand prevailing themes within large collections of financial news. By taking raw text from thousands of articles, it processes them to identify key subjects and how their prominence changes over time. The output provides insights into trends and shifts in topics relevant to the financial world, useful for those tracking market sentiment or economic narratives.
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Use this if you need to automatically identify and track recurring topics within a large volume of financial news articles to understand market narratives or economic sentiment.
Not ideal if you need real-time sentiment analysis for high-frequency trading or highly specific entity extraction from individual articles.
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Aug 29, 2017
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