Get Aliexpress product details as a json response including feedbacks, variants, shipping info, description, images, etc.,

Sentiment Analysis of product based reviews using Machine Learning Approaches. This is my Final Year B.Tech Project, 2018.

Sentiment Analysis using LSTM cells on Recurrent Networks. GloVe word embeddings were used for vector representation of words. Amazon Product Reviews were used as Dataset.

source code for signed bipartite graph neural networks(CIKM 2021)

The system deletes fake reviews on products and rates a product automatically based on customer reviews

An automatically annotated sentiment analysis dataset of product reviews in Russian.

Like reviews, only way better.

this is my repository for Amazon review helpfulness prediction model

This is a multi - vendor e - commerce website. Built with all cool features, wide range of products, invoice generations, vendor and admin portals, online payments and much more!

This is a full stack eCommerce website built using the PERN stack (PostgreSQL, Express, React, Node.js). It features a modern and responsive design, secure payment gateways, dynamic product display algorithms, and comprehensive user functionalities such as wishlist, reviews, order tracking, and more.

System for irony detection in product reviews

Sentiment analysis on product reviews with identification of most reviewed products from Amazon product reviews dataset consists of 35000 reviews.

A customer feedback demo application for collecting reviews for a product after a successful purchase.

Using Machine Learning to Analyze & Visualize Consumer Behavior

Creating customers an excellent product experience will lead to compliments, where powerful mouth-of-word was born to improve your sales performance. Reviews are also the type of “mouth-of-word” in the Net in order to test product’s quality.

Opinion Extraction based on Amazon Reviews

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.

I built Sentiment Analysis models leveraging a deep learning approach utilizing the customer reviews of Amazon products. Since Long Short Term Memory Network (LSTM) is very effective in dealing with long sequence data and learning long-term dependencies, I used it for automatic sentiment classification of future product reviews.

Reference implementation of product reviews using Episerver Social

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