Porter stemming library (C++)
Finding similarities between documents, and document search engine query language implementation
Data consists of tweets scrapped using Twitter API. Objective is sentiment labelling using a lexicon approach, performing text pre-processing (such as language detection, tokenisation, normalisation, vectorisation), building pipelines for text classification models for sentiment analysis, followed by explainability of the final classifier
Introduction to Natural Language Processing using NLTK, University of Jordan course.
This is a Movie Recommendation Project which recommend the related movie based on your watch history, it uses the Cosine-Similarity concept along with the NLP techniques to recommend the next related movie.
A PHP implementation of the English (Porter 2) Stemmer [added Protected words support]
An IR stemming project
In this project I Preprocess the movie dataset into a more convenient format, and then transform them into feature vector through CountVectorizer then Assessed word relevancy via term frequency-inverse document frequency i ALSO TRIED hashing vectorizer for memory efficienct on large data and using SDG CLASSIFER
A machine learning-powered text classification system designed to identify unwanted or malicious messages in SMS and email formats using Multinomial Naïve Bayes algorithm.
This is a project for the course "Text Mining and Search" - Master's degree in Data Science, University Milano-Bicocca
This jupyter notebook has various ML classification models to detected a mail as spam or not
Movie Recommendation using Content Filtering (Cosine Similarity) with Flask web application
Natural Language Processing Lab Experiments
Full-stack ML web app built with Python, Flask, & Scikit-Learn. Uses Cosine Similarity on movie metadata (genres, cast, keywords) to generate personalized recommendations in real-time. Features a dark-themed UI, lightweight Flask backend, & Git LFS for handling large pre-trained model files. Deployed on Render!
Implemented a Content-based recommender using CountVectorizer on 4.8K+ TMDB movies, leveraging 5K-dimensional sparse vectors & selecting Cosine similarity over Euclidean distance for NLP relevance.
This is the application which will recommend the movies to the users based on the saerch
An NLP Exploration in Video Game Analytics for Decoding Retail Sentiments
A python wrapper around surgebase's porter2 implementation.
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