I am in an ML course and one of our tasks is to predict the helpfulness of Amazon reviews. Currently, I am doing what most people seem to . That is, a hashvectorizer(2-gram) on the text, tfidf, several scalar features, and (in my case) a Ridge classifier (parameters chosen by grid search). Honestly, it has been frustrating since I have tried to read and apply best practice from the course and any published works I can find. That said, I am still only achieving marginal results:

{'Pos': 6588, 'Neg': 84412, 'TP': 2755, 'TN': 78990, 'FP': 5422, 'FN': 3833, 'Accuracy': 0.8982967032967033, 'Precision': 0.33692063103827807, 'Recall': 0.41818457802064357}


...which isn't great. I don't need help coding*, but if anyone can suggest useful case studies, or other sklearn classifiers/techniques I could research, I would be grateful. I'm rapidly running out of directions to follow up on.

Thanks!

• if relevant, you can find my latest iteration here