def print_top10(vectorizer, clf, class_labels):
    """Prints features with the highest coefficient values, per class"""
feature_names = vectorizer.get_feature_names()
for i, class_label in enumerate(class_labels):
    top10 = np.argsort(clf.coef_[i])[-10:]
print('%s: %s' % (class_label, ' '.join(feature_names[j] for j in top10)))
How to get most informative features for scikit-learn classifiers?