[82xx]

It would be easy to write software to parse that particular phrasing of that particular relationship ("Company X in Talks to Buy Company Y"), but to catch any arbitrary relationship between two entities in a given domain? That's no longer an easy problem. Not to mention that you'll need to represent that world knowledge coherently in ontologies / knowledge bases and write complex logic around it to make the data actionable.

[barrkel]

All you need is a positive correlation between an action taken on a weighted evaluation of a headline containing key words, and profit. You're overestimating the difficulty of the problem. It's model training; there's lots of historical data to tune on.

[discardorama]

> It's model training; there's lots of historical data to tune on.

Is there a timestamped archive of DowJones Newswire articles? I found it difficult to find archived news articles the last time I was looking for them.

[philjohn]

Sentiment analysis has plenty of research around it, and once you've got a big enough training and validation set, it can give very good results. My old boss works for a company that runs sentiment analysis on comments made about companies to automatically highlight positive/negative messages - it's not a massive leap from there to repurpose that to analyse positive or negative news reports about a company.