A ML pipeline to train sentiment analysis models with tweets.
Choice between a hashing vectorizer (faster) and term frequency–inverse document frequency vectorizer (more accurate).
Classifier is Naive Bayes. Uses SK-learn.
A hashing vectorizer model is saved in this repository, trained on 1 million tweets classified as positive or negative sentiments.