Developed a NLP classification model that can classify negative reviews of restaurants, help restaurant managers save time on reviewing comments, absorbing information. Analyze the service defects, help restaurants improve business

Scraping paper data, preprocessed and trained using BERT variants, deployment and an integration to website

Kaggle Challenge: User Gender Classification on Reddit using NLP.

A project on unsupervised topic modeling of garage reviews.

Create Model NLP Classification Bank Customer Complaint and Deploy to Streamlit

Estudo de caso onde o objetivo será desenvolver um modelo que seja capaz de classificar os sentimentos dos usuários do Twitter com base em seus tweets. Foi treinado um modelo com 800 mil tweets em português separados em classes positivas, negativas e neutras.

This project analyzes app reviews using sentiment classification powered by DistilBERT. It employs machine learning and natural language processing (NLP) techniques to determine sentiment as positive, neutral, or negative. The analysis provides valuable insights into user feedback for app improvement.

A bag of words analisys based on IMDB movie opinions with PySpark

News classification and recommend model

Predict which Tweets are about real disasters and which ones are not

Analysis of capsule network performance for text classification (IMDB)