Fall 2020 DS4A Project: AWS
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Updated
Oct 15, 2020 - Jupyter Notebook
Fall 2020 DS4A Project: AWS
Lightweight website to review a product.
This repository showcases the outcomes of an Exploratory Data Analysis (EDA), including visualisation, conducted on 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 and PySpark.
👩💻“Don’t do it for the money, do it because you love it!”Sound familiar?⏳ Sounds great, right?📡
The objective of the project is to detect the underlying sentiments of the product reviews. Classifying the reviews as positive, negative and neutral helps to determine the overall emotion behind the product and assist business strategies.
sentiment and part of speech analysis on product reviews
Sentiment analysis with ML to classify customers purchase reviews
Data Science - Text Mining Work
Fully connected neural network analyzing sentiments in reviews for Amazon's Alexa.
This repository is all about creating the framework for the digital banking
Web scraping product reviews data from AliExpress.
Emotionally Aware Chatbot (EAC) for Responding to Indonesian Product Reviews
Sentiment analysis of Flipkart reviews. Predict ratings. Dublin Business School research.
This repository have a basic idea on how product analysis can be done on amazon using Web Scrapping.
Investigated whether Vine reviews are free of bias using SQL and ETL skills to analyze the data.
Product review sentiment analysis use Apache Storm
Text-Mining-Amazon-Reviews-using-Scrapy. Ever wondered? Life would be easier if there could be ways to know how well your product performs and what do people feel about your product? The Solution -Text Mining Techniques. https://medium.com/@vaitybharati/text-mining-how-to-extract-amazon-reviews-using-scrapy-5bd709cb826c
Implementation of machine Learning algorithms to perform analysis like- Predictive and Sentiment analysis.
Understanding the sentiment of customers from product reviews using IndicBERT
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