A rolling version of the Latent Dirichlet Allocation.
Determine a Prototype from a number of runs of Latent Dirichlet Allocation.
Finding trends in news article with Spark (MLLIB, LDA), Spark-Solr, Solr
Todo lo accesorio y entorno al proyecto sobre Análisis de textos con R
Welcome to my repository for the British Airways Data Science Virtual Internship
Automatic topic modelling using minimal external input and computational resources
Data Analytics Project of AI Trends in Tech news using Python
Develop a model which will make it possible to identify specific subject matter being discussed/present on web pages by using a combination of web crawling and natural language processing.
Team - Brogrammers. Ideathon 2018. Won 2nd Prize
Este repositório contém scripts da minha monografia, em que analisei dados do MBL (Movimento Brasil Livre) no Youtube. Incluem um arquivo com tags de vídeos do canal, usadas para criar wordclouds e um arquivo de nós para análise no Gephi. Um segundo script realiza modelagem de tópicos com base nos comentários de vídeos selecionados.
This project tests if machine learning provides a sufficient accuracy level for predicting topic classification on unseen text. LDA and Naive Bayes algorithms used. Data cleaned and uploaded to AWS S2 storage and imported to google colab using PySpark for analysis.
Reddit-based social media analysis of the r/AskWomen community (5M+ members). Extracted 4,000+ comments using the Reddit API, applied extensive preprocessing (SpaCy, contractions, language filtering), and performed sentiment analysis (VADER), topic modeling (LDA), and text mining to uncover key themes and emotional dynamics within the community.
Create a solution that will help in identifying the type of complaint ticket raised by the customers of a multinational bank using NLP and Topic Modelling (NMF)
Customer Support Ticket Dashboard
Add a description, image, and links to the topicmodelling topic page so that developers can more easily learn about it.
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