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IMDB Movie Review Sentiment Analysis
Built an end-to-end Natural Language Processing system to classify 50,000 IMDB movie reviews as positive or negative.
The project includes a complete text preprocessing pipeline with tokenization, stop-word handling, POS-aware lemmatization, and negation preservation. I used TF-IDF vectorization with unigram and bigram features and trained and compared four machine learning models: Logistic Regression, SVM, Naive Bayes, and KNN.
The best-performing models achieved approximately 90% accuracy, with SVM and Logistic Regression producing the strongest results. I also built a Streamlit web application that allows users to enter reviews, select a trained model, and receive real-time sentiment predictions with confidence scores.
Key work:
Developed an end-to-end NLP preprocessing pipeline
Implemented TF-IDF feature extraction with n-grams
Trained and compared four classification algorithms
Evaluated models using accuracy, precision, recall, confusion matrices, and classification reports
Serialized trained models for deployment
Built a real-time Streamlit inference application
The network for creativity
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Creatives on Contra have earned over $150M and we are just getting started
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