OpenTable Reviews Classification with BERT and Transfer Learning

In this NLP project, we will classify Yelp reviews into 1-star or 5-star categories using simplified methods, utilizing the Yelp Review Data Set from Kaggle, which includes a "stars" column for ratings and user votes on "cool," "useful," and "funny" reviews.

A neural network model for sentiment analysis of movie reviews using IMDb dataset. The model is built using PyTorch and BERT as the feature extractor.

NLP tutorial on fine-tuning the BERT model to classify IMDb reviews: mixed, positive, negative.

(EEE 409 Introduction to Machine Learning Project) This project utilizes machine learning classification models to perform sentiment analysis on textual data. The system categorizes reviews into positive and negative classes, providing a quantitative evaluation of model accuracy and predictive reliability.

Implementation of Distributional Semantics & Neural Text Classification as part of COMP34711: NLP

This project solves the IMDB review classification problem, which is a case study of Deep Learning with Python (See section 6.1.3). The book has an implementaion in Keras. I re-implement it using PyTorch.

A full-stack sentiment analysis system that classifies customer product reviews into positive, negative, or neutral sentiments using NLP and machine learning.

Fitting Multinomial Naive Bayes Classifier on Amazon reviews