Project is to analyze people’s sentiment and topics about the new administration. Used Twitter API to collect tweets about President Trump. Conduct sentiment analysis to measure how positive or negative the collected tweets are, which can be an indirect measure of President Trump’s approval. Find what kinds of topics are discussed related to the…

Nonnegative matrix factorization

Minimax NMF

My Programming Assignments from the MA 544 Numerical Linear Algebra for Big Data Course from Stevens Institute of Technology

A promising Spotify Wrapped feature using Principal Component Analysis

This notebook analyzes Spotify song data and builds a recommendation system to suggest songs based on user listening behavior.

These codes are written as a part of ECE219 Large Scale Data Mining course at UCLA.

Toolbox allows to test and compare methods for Image Completion and Data Completion problems in Matlab. Presented methods use various Nonnegative Matrix Factorization and Tensor decomposition algorithms. It was based on research performed during realization of PhD.

Convolutive Matrix Factorization in Python

SNMF: Integrated Learning of Mutational Signatures and Prediction of DNA Repair Deficiencies by Goossens S, Tepeli YI, Gonçalves JP

Sentimentally analyze product reviews to predict opinion honesty.

A Natural Language Processing Project: Use NewsAPI to gather URL's of news articles, along with webscraping, gather news articles and generate a bias classifier to then run on news sources that are considered "centered" by AllSides Media to determine the validity of that classification.

Implementation of Bayesian Inference for Nonnegative Matrix Factorisation Models.Computational Intelligence and Neuroscience, 2009

source code of my paper "Feature selection and multi-kernel learning for adaptive graph regularized nonnegative matrix factorization"

Theme Supervised Nonnegative Matrix Factorization

This project shows that companies often give the wrong name to different IT jobs. As a consequence, companies may fail to attract good candidates because an applicant has a significant probability to apply for the wrong job.

Filtering drums out of music using matrix factorization and CNN ML Model

This project showcases an end-to-end workflow for topic modeling and text analysis using a variety of machine learning and natural language processing techniques. The goal of this project is to extract meaningful topics from a collection of text documents, enabling insights, categorization, and understanding of the underlying themes in the data.

Keras Non-Negative Matrix Factorization

This is an implementation of the following paper (Algorithm1) in Python for image reconstruction. "N. Gillis and S.A. Vavasis, Fast and Robust Recursive Algorithms for Separable Nonnegative Matrix Factorization"