Performed word preprocessing such as special characters text and stopwords removal as well as stemming on the review texts. Conducted feature transformation to convert text data into numerical features using TF-IDF. Built a Multinomial Naive Bayes and Logistic Regression machine learning model to predict positive and negative sentiments and achieve F1 score of around 0.85. Plotted two word clouds to see the common words used in positive and negative reviews respectively.