This project demonstrates a complete workflow for binary text classification using movie reviews from the IMDB dataset. The goal is to build a sentiment classifier that predicts whether a given review is positive or negative. The notebook covers data preprocessing, handling class imbalance, feature extraction using TF-IDF, model training with various algorithms (SVM, Decision Tree, Naive Bayes, Logistic Regression), and model evaluation using metrics like accuracy, F1 score, and confusion matrix. Hyperparameter tuning is performed with GridSearchCV, and the best model is saved for deployment.
Real-world application:
This sentiment analysis model can be deployed to automatically classify user-generated reviews, helping businesses and platforms to monitor feedback, improve customer experience, and make data-driven decisions. Today, the model is being deployed as a web app, allowing users to input movie reviews and instantly receive sentiment predictions.
For questions or collaboration inquiries, reach out via favourchung7@gmail.com or connect with me on LinkedIn.