Uses topic modeling to identify context between follower relationships of Twitter users

This repo contains unsupervised models including the Latent Dirichlet Allocation (LDA) model applied to a corpus of research papers and a clustering analysis applied to customer segmentation.

Drake Analysis: a deeper look into the discography of Canada's Rap King using various NLP techniques.

Topic modeling for NYT articles.

workflow for topic modeling a personal library of PDFs

This project contains the data, code and results used in the paper: Denter, N., Seeger, F., & Moehrle, M. G. (2022). How can Blockchain technology support patent management? A systematic literature review. International Journal of Information Management, Article 102506. Advance online publication. https://doi.org/10.1016/j.ijinfomgt.2022.102506.

Multiclass Intent Classification using MLP, LSTM, and BERT (subtask: Topic Modelling).

Python Notebooks for Collecting Tweets and Analyze their text using various text classification and clustering techniques

React Component for LDAvis

E-Commerce Comment Classification with Logistic Regression and LDA model

Automatic Keyphrase Extraction from Russian-Language Scholarly Papers in Computational Linguistics

Topic modeling on tweets from users in India over a 2-year period to discover trending topics and discussions

Topic Modelling with LDA on Journalistic Turkish Texts

LDA Topic Modelling and VADER Sentiment Analyzer

This repo contains a demonstration of the Latent Dirichlet Allocation Model (LDA) for topic modelling using Gensim Package, and pyLDAvis to visualize the LDA topics.

Extracting Topics according to relevance using LDA

A Jupyter notebook analyzing the major reasons for Tesla order cancellation and how Elon Musk's tweets correlate with order cancellation.

Unsupervised Topic Modelling project using Latent Dirichlet Allocation (LDA) on the NeurIPS papers. Built as part of the final project for McGill AI Society's Accelerated Introduction to Machine Learning Course (MAIS 202).

An academic project to use text analytics tools to analyze tweets from Twitter regarding the topic 'Technology and Innovation in UAE'

Can we learn about the Resist Movement by analyzing #resist tweets?