Top2Vec learns jointly embedded topic, document and word vectors.

Expose a Top2Vec model with a REST API.

Transform a corpus of text documents (any kind) into a map with different zoom levels and topics names to summarise sub corpus of similar docs.

We created a topic modeling pipeline to evaluate different topic modeling algorithms, including their performance on short and long text, preprocessed and not preprocessed datasets, and with different embedding models. Finally, we summarized the results and suggested how to choose algorithms based on the task.

This project is aimed to create an automated method that is able to identify emerging risks faced by multiple businesses and industries, and the trends of those risks.

A review of the most popular topic modeling techniques, featuring hands-on tutorials.

Text Mining Final Project about Twitter Topic Modeling with different models

Multilingual library for scraping, preprocessing, topic modeling, and summarization. This is basic logic for Prepo service.

Compare perception about Covid-19 Vaccine by Topics from LDA-Top2Vec mix model. Analyze with BERT-Sentiment Analysis and Word Embedding

Python - Train Top2Vec model to extract Lex Fridman podcast episodes relating to AI/ML and use Spotify API to create a playlist.

NLP related works

Celem projektu było wykonanie analizy budżetów partycypacyjnych polskich miast pod kątem istniejących w nich wspólnych tematów, trendów, zależności.

Forked from Dimo Angelov's repository for Top2Vec to add a few more features. For further information, please respectively refer to the original paper and the repository in README.md.

Topic detection to identify the main topics on MIT management papers

Different strategies for topic modeling used on large sets or notes/texts for clustering, tagging and analyzing. Written in python / jupiter lab

Do some analysis based on main AI conferences

WIP

This Python code, uses the Top2Vec library to generate topic models and create word clouds for a given input CSV file containing review data. The code cleans the reviews using the NLTK library and then generates topic models using the Top2Vec library.