The Brain News Topics Analysis dataset exploits a proprietary Large Language Model approach to monitor specific topics and their sentiment within the financial news flow for stocks. For example, an investor may want to identify all news related to the topic “innovation” for a set of companies and track their sentiment with respect to each specific topic. Similarly, another investor can be interested in tracking all news related to the topic “risks for the company” and their sentiment. For each stock and each topic, three metrics are provided using the news published within a given time interval: the volume of news relevant to the topic; the buzz, which measures the variation in the amount of news that is published for each topic; a sentiment score for the topic.