𝗠𝗼𝗱𝗲𝗹 𝗖𝗼𝗻𝘁𝗲𝘅𝘁 𝗣𝗿𝗼𝘁𝗼𝗰𝗼𝗹 (𝗠𝗖𝗣) 𝗕𝗮𝘀𝗲𝗱 𝗖𝗟𝗜 𝗔𝗜 | 𝗧𝗼𝗼𝗹 𝗜𝗻𝘁𝗲𝗴𝗿𝗮𝘁𝗶𝗼𝗻 | 𝗚𝗲𝗺𝗶𝗻𝗶 𝟮.𝟬

A free, private, open-source, and minimalist web app for discovering movies and TV shows

Professional VADER sentiment analysis on IMDb movie reviews with comprehensive evaluation and error analysis (📉,📈)

🎬 Binary Sentiment Analysis on the IMDB 50K dataset. A classic NLP pipeline utilizing NLTK for preprocessing, TF-IDF for vectorization, and Logistic Regression for high-accuracy text classification.

Movie Review Management System: Java app to add, search, list, sort, and manage movie reviews. Includes file I/O for saving and uploading reviews.

Analyzing movie reviews sentiment analysis using LSTM Model

This project performs sentiment analysis on movie reviews from the IMDB dataset using deep learning techniques. The goal is to classify user feedback as positive or negative based on the textual content of reviews. Two models are implemented: LSTM and BiLSTM, to compare their performance in understanding sequential data.

Advanced sentiment analysis system for movie reviews using machine learning. Compares Logistic Regression, Naive Bayes, and SVM models with interactive prediction interface and comprehensive text preprocessing pipeline.

Customer Feedback Analytics using NLP and Machine Learning on 50,000 IMDb Movie Reviews with TF-IDF, Bag of Words, and Logistic Regression.

Movie Review Sentiment Analysis using NLP and Machine Learning

Sentiment classification of movie reviews using NLTK and Logistic Regression

A program that classifies text as positive or negative based on IMDb movie review database.

Python-based scraper for collecting movie data from The Numbers and user reviews from IMDb, useful for text mining, sentiment analysis, and forecasting

Sentiment analysis of IMDb movie reviews using TF-IDF and Logistic Regression

A fully local RAG pipeline that answers natural language questions about movie reviews. Uses Ollama for embeddings + local LLM, Chroma for the vector store, and LangChain to retrieve, summarize, and generate answers.

Unboxing Letterboxd: An Exploratory Data Analysis of Movies, Genres, Reviews, and Audience Perception of the Female Gaze in Women-Directed Films Using Python

NLP project classifying IMDB movie reviews as positive or negative using TF-IDF and Logistic Regression. Achieved 83.5% accuracy.

Movie Review Sentiment Analysis using NLP and Machine Learning

Machine learning Model understanding Human sentiments through reviews.

Sentiment analysis on IMDb movie reviews using LSTM-based Recurrent Neural Networks (unidirectional, bidirectional, stacked) in Keras/TensorFlow. Includes visualisations, model architecture, and suggestions for future improvements.