id review name label 1 it is a great product for turning lights on. Ashley 1 2 plays music and have a good sound. Alex 1 3 I love it, lots of fun. Peter 0
The aim is to classify the text; if the review is about the functionality of the product (e.g. turn the light on, music),
How can I improve the accuracy of
LinearSVC; I tried difference models but
LinearSVC gives the highest accuracy but it is still not enough:
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.20, random_state=42) text_clf_lsvc = Pipeline([('tfidf', TfidfVectorizer()), ('clf', LinearSVC(loss='hinge', penalty='l2', max_iter = 100))])
metrics.accuracy_score(y_test,predictions) is 0.84 at this stage.
I would appreciate your advice.