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

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

Natural Language Processing Lab Experiments

Python codes for information retrieval

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

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

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

An NLP Exploration in Video Game Analytics for Decoding Retail Sentiments

A python wrapper around surgebase's porter2 implementation.

A robust framework for exploring and evaluating various information retrieval (IR) techniques. It features a command-line interface for executing different ranking algorithms, a comprehensive evaluation suite, and a modular architecture for easy extension.

A machine learning project to classify news as real or fake using NLP techniques. Includes text preprocessing, TF-IDF, and models like Logistic Regression, Naive Bayes, and SVM, with SHAP for model explainability.