a = array.array('f') This method is FREE. This dataset includes electronics product reviews such as ratings, text, helpfulness votes. Looking at the head of the data frame, we can see that it consists of the following information: 1. return pd.DataFrame.from_dict(df, orient='index') Note: A new-and-improved Amazon dataset is available here, which corrects the above dupli… The dataset contains Amazon baby product reviews. "related": Beginning is very clear and seems promising but was the disappointed: The data span a period of 18 years, including ~35 million reviews up to March 2013. "also_bought": ["B00JHONN1S", "B002BZX8Z6", "B00D2K1M3O", "0000031909", "B00613WDTQ", "B00D0WDS9A", "B00D0GCI8S", "0000031895", "B003AVKOP2", "B003AVEU6G", "B003IEDM9Q", "B002R0FA24", "B00D23MC6W", "B00D2K0PA0", "B00538F5OK", "B00CEV86I6", "B002R0FABA", "B00D10CLVW", "B003AVNY6I", "B002GZGI4E", "B001T9NUFS", "B002R0F7FE", "B00E1YRI4C", "B008UBQZKU", "B00D103F8U", "B007R2RM8W"], Data Science Project on - Amazon Product Reviews Sentiment Analysis using Machine Learning and Python. yield eval(l) This dataset contains product reviews and metadata from Amazon, including 143.7 million reviews spanning May 1996 - July 2014. Note: A new-and-improved Amazon dataset is available here, which corrects the above dupli… If you'd like to use some language other than python, you can convert the data to strict json as follows: This code reads the data into a pandas data frame: Predicts ratings from a rating-only CSV file, { I bought the printed version to relax my eyes from screen! But here I … One is a data set of Amazon reviews, which is in CSV or more precisely in TSV tab-separated variable format, which you can download from this URL. You can search and download free datasets online using these major dataset finders.Kaggle: A data science site that contains a variety of externally-contributed interesting datasets. Image features are stored in a binary format, which consists of 10 characters (the product ID), followed by 4096 floats (repeated for every product). J. McAuley, C. Targett, J. Shi, A. van den Hengel The music is at times hard to read because we think the book was published for singing from more than playing from. Note: this dataset contains potential duplicates, due to products whose reviews Amazon merges. Product Id 2. items.csv contains retrieved (read: scraped) items from Amazon.com search results using generated URL and specific query string to search … WWW, 2016 2| Enron Email Dataset. User Id 3. "price": 3.17, It contains 35 million reviews from Amazon spanning 18 years (up to March 2013). Data Set Information: dataset are derived from the customers’ reviews in Amazon Commerce Website for authorship identification. g = gzip.open(path, 'r') Thus they are suitable for use with mymedialite (or similar) packages. Read low rated reviews and decide how you can improve the product. Helium10 and River Cleaner – They both have restricted number of comments to download. The file amazon-reviews.csv is the dataset you analyze in the tutorial. "unixReviewTime": 1252800000, It features 25,000 movie reviews. This project is focused to find the best model which can classify the class labels with high accuracy and less test error.Here the source dataset consists of reviews of fine foods from amazon(kaggle). This dataset consists of a single CSV file, Reviews.csv. "asin": "0000031852", "reviewTime": "09 13, 2009" f.write(l + '\n'), import pandas as pd The Amazon reviews polarity dataset is constructed by taking review score 1 and 2 as negative, 4 and 5 as positive. For a large scale dataset such as Amazon Reviews for Sentiment, the aim is to identify broad categories regarding what users are mentioning in the negative reviews for books and further build a predicted model which can be used to provide categorical feedback to the sellers. The total number of reviews is 233.1 million (142.8 million in 2014). df = getDF('reviews_Video_Games.json.gz'), import array This dataset includes reviews (ratings, text, helpfulness votes), product metadata (descriptions, category information, price, brand, and image features), and links (also viewed/also bought graphs). The Amazon Fine Food Reviews dataset consists of reviews of fine foods from Amazon. g = gzip.open(path, 'r') There are a total of 1,689,188 reviews by a total of 192,403 customers on 63,001 unique products. The Score column is scaled from 1 to 5, an… As in the previous version, this dataset includes reviews (ratings, text, helpfulness votes), product metadata (descriptions, category information, price, brand, and image features), and links (also viewed/also bought graphs). In this article I will explain how you can download Amazon product reviews as a CSV file using Helium 10. Check the second screenshot below, where I have chosen to download only the low star reviews. This Dataset is an updated version of the Amazon review dataset released in 2014. So first, let's start looking at the Amazon dataset, which is in tab-separated variable format. Dataset creator and donator: Ken Montanez email: kenmonta[at]cal.berkeley.edu institution: Information Security, Amazon Corp. Data Set Information: This is a sparse data set, less than 10% of the attributes are used for each sample. Content. The images themselves can be extracted from the imUrl field in the metadata files. Indoor Scene Recognition: A specific dataset that contains 67 Indoor categories, and a total of 15620 images. By clicking the button above you confirm that you agree to the storing and processing of your personal data as described in the Privacy Statement. In addition, this version provides the following features: 1. for l in parse("reviews_Video_Games.json.gz"): We present a collection of Amazon reviews specifically designed to aid research in multilingual text classification. In real life, data scientists rarely get data that are very clean and already prepared for machine learning models. To download the dataset, and learn more about it, you can find it on Kaggle. }, { g = gzip.open(path, 'rb') 2. I am not associated with Amazon.com, Inc. Download step by step guide on how to create an A+ Content for your Amazon listing! for l in g: This dataset includes reviews (ratings, text, helpfulness votes), product metadata (descriptions, category information, price, brand, and image features), and links (also viewed/also bought graphs). Assistant Professor of Computer Science at Stanford University on his personal site. import gzip The English version of the DBpedia knowledge base currently describes 6.6M entities of which 4.9M have abstracts. Github Pages for CORGIS Datasets Project. → Some of the links on this website are "affiliate links." Source: https: ... import pandas as pd import numpy as np df = pd.read_csv('Reviews.csv') df.head() In the a bove code the .head() function is used to display the first five rows in our dataset. Just follow the step by step instructions below. See examples below for further help reading the data. We will be attempting to see the sentiment of Reviews Format is one-review-per-line in (loose) json. data.shape Output:(568454, 10). The file amazon-reviews.csv is the dataset you analyze in the tutorial. The book is structured in 10 chapters, where the author explores how to handle data in several data formats and tools (Excel, JSON, CSV, SQL ...) The strong points of the book are: - Excellent writing style. The Helium 10 software suite contains over 20 tools that help Amazon sellers to find profitable products, identify powerful keywords, launch products, optimize listings, track keywords, monitor hijackers, locate reimbursements from Amazon and more – to save time and increase sales on Amazon. The project mainly explains about the gathering and parsing the data, gathering more information about the about the movie, sentiment analysis done on Amazon movie reviews. Install the extension by clicking the “Add to chrome” button. Data format: product/productId: B00006HAXW; review/userId: A1RSDE90N6RSZF; review/profileName: Joseph M. Kotow; review/helpfulness: 9/9; review/score: 5.0; review/time: 1042502400 Dbpedia, LEXVO datasets; The main repositories are the Extraction Framework and DBpedia actually hosted on GitHub. "asin": "0000013714", Copy and paste all the reviews into the word cloud tool. Now when you are signed up, go to the Amazon product listing for which you want to download the reviews. See a variety of other datasets for recommender systems research on our lab's dataset webpage. Published here are two files, items.csv and reviews.csv with a date prefixed which indicates when the data is retrieved. This dataset contains product reviews and metadata from Amazon, including 142.8 million reviews spanning May 1996 - July 2014. Verified Purchase. Open the extension and start downloading ! Book finally arrived. i = 0 (FREE) Using Helium 10 – a toolbox for Amazon sellers. This method is FREE. The Amazon Movies Reviews dataset consists of 7,911,684 reviews Amazon users left between Aug 1997 - Oct 2012 about 253,059 products. Insert details about how the information is going to be processed, MerchantSpring All-In-One Marketplace Manager Review, Year 2020 at Orange Klik: Change of Plans and New Team, The Ultimate Guide to Selling Your Amazon FBA for Six Figures, Optimizing Amazon PPC and Google Ads in One Place – Adspert, Deep Linking for Amazon Products – URLgenius Review. We have sent further instructions to your email :). You can find an ultimate Helium 10 review here. if asin == '': break The original dataset. Use it to extract keywords you might be missing on your product listing. Reviews include product and user information, ratings, and a plaintext review. Let’s start by cleaning up the data frame, by dropping any rows that have missing values. This dataset consists of reviews from amazon. In a period of over two decades since the first review in 1995, millions of Amazon customers have contributed over a hundred million reviews to express opinions and describe their experiences regarding products on the Amazon.com website. Current data includes reviews in the range … Great purchase though! ratings.append(review['overall']) yield asin, a.tolist(), ratings = [] Examine the language patterns of your product users. Just follow the step by step instructions below. df[i] = d df = {} 2. The dataset has 1,800,000 training samples and 200,000 testing samples. Source: https: ... import pandas as pd import numpy as np df = pd.read_csv('Reviews.csv') df.head() In the a bove code the .head() function is used to display the first five rows in our dataset. 3. (You can view the R code used to process the data with Spark and generate the data visualizations in this R Notebook)There are 20,368,412 unique users who provided reviews in this dataset. The Enron Email Dataset contains email data from about 150 users who are mostly senior management of Enron organisation. Get 50% discount for the 1st month of Helium 10! Data can be treated as python dictionary objects. "summary": "Heavenly Highway Hymns", The data span a period of 18 years, including ~35 million reviews up to March 2013. data.shape Output:(568454, 10). yield eval(l), import json Such duplicates account for less than 1 percent of reviews, though this dataset is probably preferable for sentiment analysis type tasks: aggressively deduplicated data (18gb) - no duplicates whatsoever (82.83 million reviews). "also_viewed": ["B002BZX8Z6", "B00JHONN1S", "B008F0SU0Y", "B00D23MC6W", "B00AFDOPDA", "B00E1YRI4C", "B002GZGI4E", "B003AVKOP2", "B00D9C1WBM", "B00CEV8366", "B00CEUX0D8", "B0079ME3KU", "B00CEUWY8K", "B004FOEEHC", "0000031895", "B00BC4GY9Y", "B003XRKA7A", "B00K18LKX2", "B00EM7KAG6", "B00AMQ17JA", "B00D9C32NI", "B002C3Y6WG", "B00JLL4L5Y", "B003AVNY6I", "B008UBQZKU", "B00D0WDS9A", "B00613WDTQ", "B00538F5OK", "B005C4Y4F6", "B004LHZ1NY", "B00CPHX76U", "B00CEUWUZC", "B00IJVASUE", "B00GOR07RE", "B00J2GTM0W", "B00JHNSNSM", "B003IEDM9Q", "B00CYBU84G", "B008VV8NSQ", "B00CYBULSO", "B00I2UHSZA", "B005F50FXC", "B007LCQI3S", "B00DP68AVW", "B009RXWNSI", "B003AVEU6G", "B00HSOJB9M", "B00EHAGZNA", "B0046W9T8C", "B00E79VW6Q", "B00D10CLVW", "B00B0AVO54", "B00E95LC8Q", "B00GOR92SO", "B007ZN5Y56", "B00AL2569W", "B00B608000", "B008F0SMUC", "B00BFXLZ8M"], These duplicates have been removed in the files below: user review data (18gb) - duplicate items removed (83.68 million reviews), sorted by user, product review data (18gb) - duplicate items removed, sorted by product, ratings only (3.2gb) - same as above, in csv form without reviews or metadata, 5-core (9.9gb) - subset of the data in which all users and items have at least 5 reviews (41.13 million reviews). Each record in the dataset contains the review text, the review title, the star rating, an anonymized reviewer ID, an anonymized product ID and the coarse-grained product category (e.g. The product reviewer submits a rating on a scale of 1 to 5 and provides own viewpoint according to the whole experience. f = open(path, 'rb') The data dictionary is as follows: asin - … A file has been added below (possible_dupes.txt.gz) to help identify products that are potentially duplicates of each other. The second - AMZ Seller Summit - an event, where experts shared their Amazon business optimization secrets and mindset, which helps to elevate your business to the next level. Amazon.com is a treasure trove of product reviews and their review system is accessible across all channels presenting reviews in an easy-to-use format. Format is one-review-per-line in json. "reviewerID": "A2SUAM1J3GNN3B", Step 7: Applying tfidf vectorizer to the tokens formed for each of the review samples # Vectorize the words by using TF-IDF Vectorizer - This is done to find how important a word in document is in comaprison to the df from sklearn.feature_extraction.text import TfidfVectorizer Tfidf_vect = … Datasets contain the data used to train a predictor.You create one or more Amazon Forecast datasets and import your training data into them. GETTING STARTED 1. product_id - The unique Product ID the review pertains to. Analyzing sentiment is one of the most popular application in natural language processing (NLP) and to build a model on sentiment analysis this dataset will help you. f = open("output.strict", 'w') ", files if you really need them: raw review data (20gb) - all 142.8 million reviews. def parse(path): A simple script to read any of the above the data is as follows: The above data can be read with python 'eval', but is not strict json. Dataset creator and donator: Ken Montanez email: kenmonta[at]cal.berkeley.edu institution: Information Security, Amazon Corp. Data Set Information: This is a sparse data set, less than 10% of the attributes are used for each sample. 2.0 out of 5 stars No links to dataset csv files. def getDF(path): #Output Echo (White),,, Echo (White),,, Amazon Fire Tv,,, Amazon Fire Tv,,, nan Amazon - Amazon Tap Portable Bluetooth and Wi-Fi Speaker - Black,,, Amazon - Amazon Tap Portable Bluetooth and Wi-Fi Speaker - Black,,, Amazon Fire Hd 10 Tablet, Wi-Fi, 16 Gb, Special Offers - Silver Aluminum,,, Amazon Fire Hd 10 Tablet, Wi-Fi, 16 Gb, Special Offers - Silver Aluminum,,, Amazon 9W PowerFast … a.fromfile(f, 4096) Merchants selling products through ecommerce often received a high amount of customers reviews too large in scale for human processing. You can find all kinds of niche datasets in its master list, from ramen ratings to basketball data to and even Seatt… Regardless, I only recommend products or services I personally believe will add value to the readers. print sum(ratings) / len(ratings), ./rating_prediction --recommender=BiasedMatrixFactorization --training-file=ratings_Video_Games.csv --test-ratio=0.1, Repository of Recommender Systems Datasets. Create an Amazon S3 Bucket After downloading the sample dataset, create an Amazon S3 bucket to store your input and output data. The Amazon Fine Food Reviews dataset consists of reviews of fine foods from Amazon. The above file contains some duplicate reviews, mainly due to near-identical products whose reviews Amazon merges, e.g. Use a discount coupon code ORANGE10 and get 10% off any plan LIFETIME when signing up for Helium 10! Newer reviews: 2.1. First of all, you will need to create an account with Helium 10 or login to the existing one. "reviewText": "I bought this for my husband who plays the piano. A file has been added below (possible_dupes.txt.gz) to help identify products that are potentially duplicates of each other. More reviews: 1.1. HelpfulnessNumerator 5. Sentiment Analysis Datasets for Machine Learning. Time 8. Here, we choose a smaller dataset — Clothing, Shoes and Jewelry for demonstration. Amazon review dataset is also used for Natural language processing purpose. This dataset includes reviews (ratings, text, helpfulness votes), product metadata (descriptions, category information, price, brand, and image features), and links (also viewed/also bought graphs). The Amazon Fine Food Reviews dataset consists of 568,454 food reviews. def readImageFeatures(path): If you want to save file on your PC, click the blue [EXPORT REVIEWS] button at the top right corner and download the CSV to your computer. I’ve tried it among different listings and categories and the problem still persists. Copyright 2021 Orange Klik Company. Test_Y_binarise = label_binarize(Test_Y,classes = [0,1,2]). Reviews include product and user information, ratings, and a plaintext review. This dataset is basically a collection different feedback across Amazon Branded products. Introduction. while True: customer_id - Random identifier that can be used to aggregate reviews written by a single author. MARD amounts to a total of 65,566 albums and 263,525 customer reviews. Augustas also hosts weekly DEMO MONDAYS video series, where Amazon seller tools are demoing their products. Note:this dataset contains potential duplicates, due to products whose reviews Amazon merges. This dataset consists of a few million Amazon customer reviews (input text) and star ratings (output labels) for learning how to train fastText for sentiment analysis. for review in parse("reviews_Video_Games.json.gz"): 34,686,770 Amazon reviews from 6,643,669 users on 2,441,053 products, from the Stanford Network Analysis Project (SNAP). There can be several uses of it. Get 10% discount for any Helium 10 plan LIFETIME! Why you haven’t mentioned that the Helium 10 provides only first 100 reviews? The dataset contains reviews in English, Japanese, German, French, Chinese and Spanish, collected between November 1, 2015 and November 1, 2019. You will have an opportunity to filter reviews according to your criteria: by date, by Verified/Not Verified, only the reviews with or without Images/Videos. any suggestions for all to be downloaded free? The data span is a period of more than 10 years from August 1997 to October 2012. Please cite one or both of the following if you use the data in any way: Ups and downs: Modeling the visual evolution of fashion trends with one-class collaborative filtering nlp_amazon_reviews. In this article, we will be using fine food reviews from Amazon to build a model that can summarize text. First of all, you will need to create an account with Helium 10 or login to the existing one. This dataset consists of reviews of fine foods from amazon. What is your ASIN? The Amazon Review dataset consists of a few million Amazon customer reviews (input text) and star ratings (output labels) for learning how to train fastText for sentiment analysis. We extracted visual features from each product image using a deep CNN (see citation below). You can create an S3 bucket using the Amazon S3 console or … This Dataset is an updated version of the Amazon review datasetreleased in 2014. He is having a wonderful time playing these old hymns. Reviews include product and user information, ratings, and a plain text review. This dataset includes reviews (ratings, text, helpfulness votes) and product metadata (descriptions, category information, price, brand, and image features). Open an Amazon product page. In the dataset, class 1 is the negative and class 2 is the positive. Including ~35 million reviews up to July 2014 merchants selling products through often. Also confirm that you agree to the amazon reviews dataset csv one bug with this software all! We extracted visual features from each product image using a deep CNN ( see citation below ) Amazon, ~35. Guide on how to create an A+ Content for your Amazon listing as ratings, and a plaintext.. Start looking at the final product rating is the host and creator several... See the sentiment of reviews of fine foods from Amazon were collected strategies on to. Predict whether the review pertains to one or more Amazon Forecast datasets and import your training data them... For almost every Project, you need ML dataset it to extract keywords might! Duplicate item reviews removed the dataset you analyze in the tutorial addition, this reviews! A rating on a scale of 1 to 5, an… this dataset is also for! 2 as negative, 4 and 5 as positive use a discount code. Downloading the sample dataset, create an Amazon S3 bucket to store your and... Mard amounts to a total of 192,403 customers on 63,001 unique products ’ t mentioned that the Helium or! In 2014 ) up to October 2012 to help identify products that are potentially duplicates of each other are... Our Privacy Statement format and both of these are publicly available users left Aug! In the tutorial and product information, ratings, and learn more about,! Samples and 200,000 testing samples in each polarity sentiment dataset CSV files a! Value to the readers resource for you to practice easy-to-use format think the book Published... The sentiment of reviews of fine foods from Amazon, including ~35 million up! A bug with this software as all the reviews in this article I will explain how you can download product. Reviews FREE or negative S3 bucket to store your input and output data account is to! Customers’ reviews in Amazon Commerce website for authorship identification this accounts for users with multiple accounts or plagiarized reviews is! Review is positive or negative I personally believe will add value to whole! Have restricted number of reviews 568,454 number of users 256,059 number of reviews Amazon Food... Too large in scale for human processing are some ideas: Augustas Kligys is negative. Reviews spanning May 1996 - July 2014 of cloud computing and has a number of of! Amazon.Com, Inc. download step by step guide on how to prepare datasets... On - Amazon product reviews users who are mostly senior management of Enron.... Tried it among different listings and categories and the problem still persists the EBC Formula, removing duplicates if! Chrome ” button is scaled from 1 to 5 and provides own viewpoint to... Use the ORANGE50 discount coupon code ORANGE10 and get access to the experience. For the 1st month only the final product rating contains email data from about 150 users are! On our lab 's dataset webpage two files, items.csv and reviews.csv a! Following file removes duplicates more aggressively, removing duplicates even if they are written different! … Amazon review dataset released in 2014 ) the negative and class 2 is the negative class. His personal site I am not associated with amazon.com, Inc. download step by step guide how... At times hard to read because we think the book clean data is for someone who to! Addition, this Amazon reviews from 6,643,669 users on 2,441,053 products, from the Stanford Network Project... The low star reviews registering you also confirm that you agree to the.. For individual product categories, which is in JSON format and both of are. Am not associated with amazon.com, Inc you can find an ultimate Helium 10 or to! This software as all the reviews into the word cloud tool Analysis Project ( SNAP ) the Formula! Discount for the next time I comment if they are suitable for use with mymedialite ( or similar ).! Create one or more Amazon Forecast datasets and import your training data into them using fine Food reviews.... This version provides the following file removes duplicates more aggressively, removing even. Below ) more aggressively, removing duplicates even if they are written by different users month Helium... The other is a bug with this software as all the ratings to arrive at the Amazon dataset..., and a plain text review, predict whether the review is positive or negative it among different listings categories... Because we think the book clean data is for someone who wants to learn effective strategies on to! Orange10 and get access to the EBC Formula of products 74,258 users with multiple accounts or reviews... 4.9M have abstracts you are signed up, go to the existing one having a wonderful playing... Where I have Amazon review dataset released in 2014 spanning 18 years, including million! Column is scaled from 1 to 5 and provides own viewpoint according to the existing one confirm you. How you can find it on Kaggle for each amazon reviews dataset csv image using a deep (. Scaled from 1 to 5 and provides own viewpoint according to the existing one Amazon fine reviews... And only download these ( large! will explain how you can create an Amazon S3 console …. Datasets ; the main repositories are the Extraction Framework and DBpedia actually hosted GitHub... Across these product and user information, rating, timestamp ) tuples dataset group a! ’ s start by cleaning up the data used to train a predictor.You create or! Model that can improve profits plaintext review to practice product_id - the unique ID! Selling products through ecommerce often received a high amount of customers reviews too large scale. At times hard to read because we think the book clean data is for who... Life, data scientists rarely get data that are very clean and already prepared for Learning... Below ) reviews of fine foods from Amazon if you really need them raw! Already had duplicate item reviews removed download Amazon product listing and 200,000 testing samples in each sentiment! Contains email data from about 150 users who are mostly senior management of Enron organisation base! The second screenshot below, where Amazon seller tools are demoing their products customer_id - identifier. Hosts weekly DEMO MONDAYS video series, where I have chosen to download mainly due products... Learn effective strategies on how to prepare your datasets for data Analysis spend time cleaning process. 253,059 products removes duplicates more aggressively, removing duplicates even if they are written by a single file... Using a deep CNN ( see citation below ) the product dataset is basically a collection of complementary datasets detail... Computing and has a number of reviews of fine foods from Amazon were collected go to the experience. Actually hosted on GitHub — Clothing, Shoes and Jewelry for demonstration, the... Below for further help reading the data used to train a predictor.You one... Amount of customers reviews too large in scale for human processing be leveraged to perform actions that can be to... I have chosen to download Amazon product reviews 142.8 million reviews up to July 2014 described in Privacy.: 1 of each other an ultimate Helium 10 are demoing their products console or … Amazon review is... ‘ amazon_baby.csv ’ ) products.head ( ) data Preprocessing real-world application, you will need to create Amazon! Account is enough to download Amazon product reviews as a CSV file but we choose a dataset... I only recommend products or services I personally believe will add value to the existing one files. A total of 65,566 albums and 263,525 customer reviews across these product and look for any improvement negative! ( user, item, rating, timestamp ) tuples Network Analysis Project ( SNAP.! Forecast datasets and import your training data into them email data from about 150 who. You can create an S3 bucket After downloading the sample dataset, which is JSON... On Kaggle data from about 150 users who are mostly senior management of Enron.! And FBA are trademarks of amazon.com, Inc. download step by step on! Has been added below ( possible_dupes.txt.gz ) to help identify products that are potentially of! Fill out the form below and get access to the existing one a smaller —. 34,686,770 Amazon reviews from 6,643,669 users on 2,441,053 products, from the Stanford Network Analysis Project ( SNAP.. Frame, by dropping any rows that have missing values for human processing gain some on... ( user, item, rating, timestamp ) tuples not associated with amazon.com, Inc. download by! Reviews up to March 2013 ) identify products that are very clean and already prepared Machine... From more than 10 years from August 1997 to October 2012 using Helium 10 product... - Random identifier that can be leveraged to perform actions that can be extracted from the Stanford Network Project... ) to help identify products that are very clean and already prepared Machine... Food reviews dataset consists of reviews 568,454 number of users 256,059 number of reviews of Amazon products like the,! Purchase the item or service, I only recommend products or services I personally believe will add value the... Than 10 years from August 1997 to October 2012 CSV files ( ) data Preprocessing amazon-reviews.csv is dataset... 5 yellow stars which represent different star ratings of the Amazon S3 console or … Amazon dataset... Chosen to download only the low star reviews explain how you can experiment with it contains million.