Full stack web application for restaurant billing management system
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.
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.
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NLP demos and talks made with Jupyter Notebook and reveal.js
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Cutomer reviews analysis and segmentation. Create suggestions for app improvement based on the clustered reviews.
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