Leveraging BERT and c-TF-IDF to create easily interpretable topics.

This project focuses on analyzing tweets from Twitter using topic modeling techniques and interactive visualizations. It employs Latent Dirichlet Allocation (LDA) to discover topics within the tweet data and generates interactive word clouds based on topic-term strengths derived from the model. Users can explore topics and related tweets interactiv

Analysis of Google PlayStore reviews for “League of Legends: Wild Rift” and “Mobile Legends: Bang Bang”

My published paper on the application of LDA on documents. Base corpus: Thousands of LDS General Conference articles spanning decades.

This repository showcases an interactive conversational analysis of the Cornell Movie-Dialogs Corpus, visualised using D3.js, as part of the Data Analysis & Visualisation (DS3001) course final project, encompassing 220,579 exchanges between 10,292 character pairs in 617 movies.

Top2Vec learns jointly embedded topic, document and word vectors.

Analysis of censored tweets. Undestanding the topics that are censored in different countries using different NLP techniques

Topic Modelling & Sentiment Analysis of Data Science Subreddit

A set of methods for finding an appropriate number of topics in a text collection

Topic modelling data collection and analysis with Python for LLaMA dataset

Interface for easier topic modelling.

Abarth Electrification | Bayes Business School | MSc Business Analytics | Applied Research Project

X-Analyze : An Insightful Twitter/X User Profile Analyser

Text mining: Using LDA to create a topic model to cluster news articles

A repository contains necessary foundational exercises in NLP for beginners.

Various tutorials in English

Topic modelling with LDA on the tweets from the POTUS

Analysing the state of the UK Economy using Sentiment Analysis and Topic Modelling on Twitter.

Multimodal and multilingual topic model with pretrained embeddings