Sentiment Analysis of Product Reviews using NLP

Classifies e-commerce product reviews as positive, negative, or neutral.

A popular product can collect thousands of reviews, far more than any seller can read, and the star rating alone hides why customers are pleased or annoyed. A four-star product may have a recurring complaint about battery life or delivery that only shows in the text. This project uses natural language processing to classify reviews as positive, negative or neutral and to summarise what customers say.

Reviews are loaded with Pandas and cleaned by lowercasing, removing markup and punctuation, handling negations such as "not good", removing stop words with NLTK and lemmatizing words to their base form. The text is converted into TF-IDF features, including two-word phrases such as "battery life". Naive Bayes, logistic regression and a linear support vector machine are trained and compared using accuracy and the macro F1 score, which matters because neutral reviews are harder to classify than extreme ones. A Streamlit dashboard lets a user pick a product and see the share of each sentiment over time, the most frequent terms in negative reviews and example reviews for every class, so that the numbers can be checked against real text.

You will learn text preprocessing, feature extraction, model comparison and how to turn a model into a useful analytics tool. The Project Reference Guide explains every step, including the limits of sentiment analysis such as sarcasm, and the Reference Implementation includes the notebooks, trained model and dashboard.