• 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.

Link to Notebook

• NLP
• Machine Learning
• Data Cleaning
• Classification