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

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.

Introduction to Natural Language Processing using NLTK, University of Jordan course.

Natural Language Processing Lab Experiments

A machine learning-powered text classification system designed to identify unwanted or malicious messages in SMS and email formats using Multinomial Naïve Bayes algorithm.

A PHP implementation of the English (Porter 2) Stemmer [added Protected words support]

This is a project for the course "Text Mining and Search" - Master's degree in Data Science, University Milano-Bicocca

Python codes for information retrieval

This jupyter notebook has various ML classification models to detected a mail as spam or not

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

An IR stemming project

Movie Recommendation using Content Filtering (Cosine Similarity) with Flask web application

A python wrapper around surgebase's porter2 implementation.

An NLP Exploration in Video Game Analytics for Decoding Retail Sentiments

This is the application which will recommend the movies to the users based on the saerch

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.