GitHub topics: amazonreviews

Repositories

Amir-Tav/NLP-Sentiment-Analysis-

Sentiment analysis using NLP techniques on Amazon product reviews. It covers text pre-processing, visualization, and basic sentiment classification.

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suvajit-patra/RNN-assignment

Sentiment Analysis on Amazon 2018 customer review dataset

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NielBohr/amazon-review-scraper

The scraper program build on Python to get all reviews under all products of a search keyword. Use chrome to work

Language: Python - Size: 8.79 KB - Last synced at: about 1 year ago - Pushed at: about 1 year ago - Stars: 0 - Forks: 0

abhinavthapper31/Sentiment-Analysis-on-Product-Reviews

Text Classification Problem : Wrote a module to classify Amazon-Product Reviews as favourable/unfavourable. Achieved accuracy of 78% and an F1 score of .81 using Logistic Regression on a test-train split of 20%, where total records were around 50000.

Language: Python - Size: 11 MB - Last synced at: over 1 year ago - Pushed at: about 6 years ago - Stars: 1 - Forks: 0

letus21500/Amazon-Electronic-Reviews

Test the genuinity of reviews.

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paolazola/Cross-source-cross-domain-sentiment-analysis

Labeled data for cross-source cross-domain sentiment classification

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Saurabhkg03/Amazon-reviews-extract

Amazon reviews extract/scrape by using BeautifulSoup, Splash JS, Docker, Python.

Language: Python - Size: 34.2 KB - Last synced at: almost 2 years ago - Pushed at: almost 2 years ago - Stars: 0 - Forks: 0

sayaliwalke30/BigDataAnalysis-RecommenderForAmazon

Built a recommender system using Apache Mahout machine learning library carried out data analysis using Hadoop, Apache Hive & Pig on Amazon Customer Reviews Data set(130M+ reviews))

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Elliott-dev/Big-Data-Challenge--Amazon-Shoppers-Product-Reviews

In this assignment I will put my ETL skills to the test. Many of Amazon's shoppers depend on product reviews to make a purchase. Amazon makes these datasets publicly available. However, they are quite large and can exceed the capacity of local machines to handle. One dataset alone contains over 1.5 million rows; with over 40 datasets, this can be quite taxing on the average local computer. My first goal for this project will be to perform the ETL process completely in the cloud and upload a DataFrame to an RDS instance. The second goal will be to use PySpark or SQL to perform a statistical analysis of selected data.

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roshancyriacmathew/Deep-Learning-on-Amazon-Product-Reviews

This project demonstrates how to perform sentiment analysis using deep learning on Amazon product reviews dataset. The dataset used for the project is obtained from Kaggle and consists of nearly 3000 reviews of amazon users regarding various amazon Alexa products like Alexa echo, Alexa dot etc. Exploratory data analysis is performed on the dataset to analyse various columns and the data is visualized using count plots and pie charts. The reviews are then processed using various methods which involve lowercase conversion, URL removal, punctuation removal, tokenization, stop word removal and stemming. The processed data is then separated into positive and negative reviews and are then visualized using Word clouds, as word clouds help to identify the most prominent/frequently used words. The processed data is then passed into a neural network, where the network learns from the data. The accuracy of the model is then measured by running the model on the test data.

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