It would be fascinating to see which words had most predictive value in each direction.
Idea: Divide old news stories into ones that mattered and didn't, and then use that to train a filter to use on current stories.
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We've published 250K articles with attention data with a CC license if someone wants to use
@kaleidanet to do that https://kaleida.github.io/attention-index/ …
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Matter to whom?
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I had almost similar idea 3 weeks back.
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Who decides what mattered?
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I was wondering the same thing. Hopefully not necessarily by views or likes. Regardless, it is an interesting experiment.
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Historical importance? Perhaps define importance based on quantity of future mentions or link backs? Like academic papers importance.
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you can easily do that with http://www.monkeylearn.com/
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Easily replicable with
@monkeylearn An example: https://monkeylearn.com/blog/analyzing-10-years-of-startup-news-with-machine-learning/ …Thanks. Twitter will use this to make your timeline better. UndoUndo
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But it can work to gather more intelligent insights from Startup news. https://monkeylearn.com/blog/analyzing-10-years-of-startup-news-with-machine-learning/ …
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