how to classify tweets in python based on handful of domains
I don't see your sentiment analysis code in your tweet_sentiment.py, so I will not comment on that.

And if I understand correctly, you are trying to build a classifier for text classification based on Twitter data.

So, depending on your data size, here are two things you could try.


Small data size (several tens of thousands or lower): Tdidf + Support Vector Machine, basically convert your text to a numerical vector form representing the existence of certain words along with their importance relative to the text and the whole dataset. Then feed the data into an SVM classifier, lots of tutorials are out there, and here is a good one.
Big data size (hundreds of thousands and more)(and ready to have fun): (word embedding Or one-hot encoding) + (CNN Or RNN)
, word embedding could represent the semantic similarity with others into a vector space model and have a dense representation, a good explanation could be found here. As for using CNN to do text classification you could refer to this and this Text understanding from scratch. and another one for RNN