Sentiment prediction using supervised learning

supervised classification using Multinomial Naive Bayes & Randon Forest

classification_v1.py implemented Bag of words, TF-IDF(with normalization)weighting, removed stop-words for feature engineering. classification_v2.py uses sklearn to process text.

used cross-validation's split_train_test() to generate hold out test set

print out performance metrics includes: accuracy, confusion matrix

plot ROC curve with Matplotlib

How to run?

$ python classfition_v1.py