Get Aliexpress product details as a json response including feedbacks, variants, shipping info, description, images, etc.,
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.
Sentiment Analysis of product based reviews using Machine Learning Approaches. This is my Final Year B.Tech Project, 2018.
source code for signed bipartite graph neural networks(CIKM 2021)
An automatically annotated sentiment analysis dataset of product reviews in Russian.
The system deletes fake reviews on products and rates a product automatically based on customer reviews
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!
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.
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.
Using Machine Learning to Analyze & Visualize Consumer Behavior
Opinion Extraction based on Amazon Reviews
Reference implementation of product reviews using Episerver Social
Magento 2 Better Product Reviews GraphQL is now a part of the Mageplaza Better Product Reviews extension that adds GraphQL features. This is helpful for PWA compatibility. The module provides type and resolver information for the GraphQl module to generate catalog review information endpoints.
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. |
