Determining helpful Amazon feedback
Naive Bayes is almost always my first go to for a problem with text data. This is primarily due to the independent distributions that integrate so well with the document by feature  independent style matrix by nature. I would extend your idea of Ridge with Lasso or ElasticNet. I would not recommend normalize your data if it is sparse, this has been known to cause problems, but StandardScaler is a rather conventional way to get a centered dataset without producing disharmony among the variables in the data. I would consider your error metric of precision or fmeasure under each of the above models as my personal next step, then do some visualizations to argue your best fit.