Amazon Review Scraper for hassle-free review data extraction, including authors, titles, descriptions, ratings, dates, and more.

A basic python 3 based web scraper for extracting reviews from Amazon. Built using Selectorlib and requests

Implementing different RNN models (LSTM,GRU) & Convolution models (Conv1D, Conv2D) on a subset of Amazon Reviews data with TensorFlow on Python 3. A sentiment analysis project.

🤩 Python Package for Scraping Amazon Product Reviews ✨

Data analytics, exploration, sentiment analysis and topic analysis (LDA) on Amazon customer reviews. And cool interactive plots.

This project performed sentimental analysis based on opinion words (like good, bad, beautiful, wrong, best, awesome, etc) of selected opinion target ( like product name for amazon product reviews).

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

🛍️📊 Effortlessly extract Amazon reviews using Python with the amazon-reviews-extraction script. This script makes use of popular Python modules like requests, pandas, bs4, and lxml to scrape and parse HTML content from Amazon product review pages. Simplify your data extraction process and gain valuable insights from customer reviews. 🐍🔍

Sentiment analysis on mobile phone reviews on amazon.

The BERT Product Rating Predictor is a natural language processing model based on the Bidirectional Encoder Representations from Transformers (BERT) model developed to predict star ratings for textual product reviews. 2020.

This project focuses on sentiment analysis of Amazon product reviews using machine learning and natural language processing techniques. 💬🔍📈

Abstractive and Extractive summarization of Amazon Reviews. Chrome extension front end

Code for SMERTI for Semantic Text Exchange.

Deep learning model for gender classification on texts using pretrained BERT models

This package allows you to search for products on Amazon and extract some useful information (ratings, number of comments).

In this project, we compared Spanish BERT and Multilingual BERT in the Sentiment Analysis task.

Sentiment Analysis on Amazon Reviews Dataset in PyTorch

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.

Implementation of classical SVM and deep Seq-to-Seq LSTM models to analyze and classify sentiment (1-5 scale) on Amazon reviews.

To build a recommendation system to recommend products to customers based on the their previous ratings for other products