Displays all the 2019 CVPR Accepted Papers in a way that they are easy to parse.

A workshop on analyzing topic modeling (LDA, CTM, STM) using R

Topic Modeling using LDA and NMF in Python

DFT simulation of He atom

data, metadata, tools, and LDA experiments on a corpus of Sanskrit philosophy texts

Topic modeling for NYT articles.

Investigate the impact of general news headlines on Stock Indices

AI model for summarizing and categorizing news articles

Keyword Extraction for PDFs

In an era marked by global security challenges, the "TAFRAS" emerges as a cutting-edge solution to tackle the ever-evolving threat of terrorism. The project is grounded in the urgent need for predictive systems that can anticipate, assess, and mitigate potential terrorist activities.

NLP analysis of Korean food discussions on Reddit using topic modeling (LDA), word clouds, and sentiment analysis

Analysis is a web application to show up the topics model I built using LDA (Latent Dirichlet Allocation) in order to analyze Andalusian parliamentarians interventions between 2008 and 2012.

a collection of short stories from project gutenberg

A quick reference guide for creating prediction models using R caret.

Repozytorium zawiera materiały omawiane w ramach warsztatu "Analiza języka naturalnego w R", prowadzonego podczas konferencji "Machine Learning@Enterprise 2018" (18 września 2018 r.).

A project utilizing NLP techniques and analysis including text mining, document term matrices, sentiment analysis, wordclouds and topic modeling with LDA.

turma v06

This project uses machine learning to categorize and prioritize airline user tweets based on content and sentiment. The goal is to reduce airlines' workload and provide personalized, empathetic responses to users. By training a sentiment analysis model, airlines can better understand customers' needs and improve their overall service on Twitter.

Explore my Document Clustering and Theme Extraction project, offering effective tools for organizing and extracting valuable insights from extensive text datasets. The objective is to provide a systematic approach to comprehend and organize unstructured text data.