i built a news sentiment tool and needed to know whether the thing actually works, so i collected four months of headlines and scored them with an llm. putting the archive out because a claim nobody can check is worth very little.
25,548 rows, 51 stocks and cryptocurrencies, 13 may to 19 september 2026. columns are ticker, timestamp, source, language, headline, url, sentiment, and which scoring model produced it.
things worth knowing before you use it
the rows are not homogeneous. there are three collection regimes and each row says which one it belongs to. until 16 august my relevance filter recognised profits in five languages and had no word for losses in any of them, so sentiment before that date is biased upward by construction. i kept those rows instead of quietly deleting them because you cannot judge what you cannot see.
seventeen tickers have under 100 rows and they are thin for two different reasons. some were never in the collection vocabulary at all. others are searched for but their name is an ordinary english word, near, cosmos, avalanche, optimism, and the relevance filter correctly throws most matches away. for those the honest sentence is not “little news about this asset”, it is “i cannot separate news about this asset from news that uses this word”.
only gdelt rows are included. i also hold ecb, esma and alpha vantage data but their licences differ and bundling three licences under one attribution line would force you to trust instead of check.
https://huggingface.co/datasets/cheruvo/financial-news-sentiment
submitted by /u/MicheleSanta
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