amazonreviewdataset
There are 9 repositories under amazonreviewdataset topic.
NisAr-PakhtoOn/Amazon-Reviwes-Naive-Bayes-and-SVM-
This project is about classifying text, the amazon reviews data has been taken, used SVM and Naive Bayes for classification.
AvatarJoshi/amazon_vine_analysis
Perform ETL on amazon reviews dataset using AWS, PostgreSQL, GoogleColab, and PySpark
ashley-green1/NLP_Sentiment_Analysis
Leveraging 21,000+ Amazon Reviews to conduct Natural Language Processing (NLP), Sentiment Analysis & Supervised Machine Learning to select the best specialty ice cream flavor for our expansion.
bhattji007/amazon_product_reviews_analysis
This GitHub repository showcases sentiment analysis in Python using VADER and the Roberta Pretrained Model. Explore two powerful techniques for analyzing sentiment in textual data and gain insights into the sentiment behind amazon reviews.
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.
Magzzie/Amazon_Vine_Analysis
Analyzing Amazon reviews written by members of the paid Amazon Vine program using AWS RDS and PySpark.
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.
Jaswanth-Batturi/seq2seq-model-using-attention
Sequence to sequence model for different tasks like machine translation, summarization, question answering, etc using attention layer implementation.
suvajit790/RNN-assignment
Sentiment Analysis on Amazon 2018 customer review dataset