If-elif loops definitely get you part of the way there; Lets say we're trying to find the tweets that actually come from people affect by a disaster, rather th

If-elif loops definitely get you part of the way there; Lets say we're trying to find the tweets that actually come from people affect by a disaster, rather than just supplying commentary about them. You could just say "if tweet is not within x distance of disaster, then tweet is likely to be commentary rather than from someone actually affected". However this could always miss a tweet like "My elderly mother just called me, and she's in trouble but doesn't know who to contact" So really this is a case of "Given tweet is not within x distance, likelihood of being from affected person is lower", but of course we don't know what the affect on likelihood is (I couldn't even ballpark it for you). If you had some classified data though, you could start to get and idea of what all these Bayes Factors are. And just like that, you're doing a naive bayesian classifier. As I said, not fancy "Deep learning with CNNs", but ML nonetheless.