You can extract your features using a Vectorizer/CountVectorizer/TfidfVectorizer/DictVectorizer, and you are using a linear model. You can use this code for binary classification:
def show_most_informative_features(vectorizer, clf, n=20):
feature_names = vectorizer.get_feature_names()
coefs_with_fns = sorted(zip(clf.coef_[0], feature_names))
top = zip(coefs_with_fns[:n], coefs_with_fns[:-(n +1):-1])
for (coef_1, fn_1), (coef_2, fn_2) in top:
print("\t%.4f\t%-15s\t\t%.4f\t%-15s" %(coef_1, fn_1, coef_2, fn_2))
Hope this answer helps.