Customer reviews sentiment analysis with Python and NLP. Generates a synthetic dataset of positive, neutral, and negative reviews, applies preprocessing (tokenization, stopwords, lemmatization), and builds TF-IDF features. Trains classifiers (Naive Bayes, Logistic Regression, Random Forest) with evaluation, confusion matrix and top features.

Full stack web application for restaurant billing management system

Customer Review Analysis is a prototype open source platform to turn the customer feedbacks in to visualization and extract the trending keywords.

this is my repository for Amazon review helpfulness prediction model

Scraping functions for (1) Amazon customer reviews and (2) product information from best sellers list

This repository includes a web application that is connected to a product recommendation system developed with the comprehensive Amazon Review Data (2018) dataset, consisting of nearly 233.1 million records and occupying approximately 128 gigabytes (GB) of data storage, using MongoDB, PySpark, and Apache Kafka.

Epinions Annotated Reviews Dataset

This repository contains a Python-based sentiment analysis project using Jupyter Notebook to analyze customer reviews. The project leverages TextBlob for sentiment classification, pandas for data manipulation, and Matplotlib/Seaborn for visualizing insights. Key features include: Sentiment Classification,Statistical Analysis

This demo repository demonstrates how to analyze customer reviews with Azure OpenAI Service (AOAI). I leveraged "ASOS Customer Review" from Kaggle to obtain valuable insight from the customer review content.

A tool for analyzing Google Play Store reviews

OpenTable Reviews Classification with BERT and Transfer Learning

The project aiming to extract product defects and opinions from customer reviews by using text clustering and sentiment analysis.

Official Fera API ruby SDK gem to make interfacing with your business's reviews easy.

Big Data Projects

Fera.ai Magento 2 Extension

NLP demos and talks made with Jupyter Notebook and reveal.js

Multi-architecture sentiment analysis (LSTM, CNN, FNN) with GloVe embeddings and Bayesian hyperparameter optimization. Comprehensive evaluation of e-commerce review classification with F1-score metrics.

An interactive Tableau dashboard that explores airline customer reviews to uncover passenger satisfaction, sentiment trends, and service quality insights.