Context

This Dataset is an updated version of the Amazon review dataset released in 2014. 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 addition, this version provides the following features:

• More reviews:

  • The total number of reviews is 233.1 million (142.8 million in 2014).

• New reviews:

  • Current data includes reviews in the range May 1996 - Oct 2018.

• Metadata: - We have added transaction metadata for each review shown on the review page.

  • Added more detailed metadata of the product landing page.

Acknowledgements

If you publish articles based on this dataset, please cite the following paper:

• Jianmo Ni, Jiacheng Li, Julian McAuley. Justifying recommendations using distantly-labeled reviews and fined-grained aspects. EMNLP, 2019.

Buy Amazon datasets and get access to over 300 million records from any Amazon domain. Get insights on Amazon products, sellers, and reviews.

This dataset contains product reviews and metadata from Amazon, including 142.8 million reviews spanning May 1996 - July 2014.

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).

We present a collection of Amazon reviews specifically designed to aid research in multilingual text classification. The dataset contains reviews in English, Japanese, German, French, Chinese and Spanish, collected between November 1, 2015 and November 1, 2019. 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. 'books', 'appliances', etc.)

Amazon-Fraud is a multi-relational graph dataset built upon the Amazon review dataset, which can be used in evaluating graph-based node classification, fraud detection, and anomaly detection models.

Dataset Statistics

Graph Construction

The Amazon dataset includes product reviews under the Musical Instruments category. Similar to this paper, we label users with more than 80% helpful votes as benign entities and users with less than 20% helpful votes as fraudulent entities. we conduct a fraudulent user detection task on the Amazon-Fraud dataset, which is a binary classification task. We take 25 handcrafted features from this paper as the raw node features for Amazon-Fraud. We take users as nodes in the graph and design three relations: 1) U-P-U: it connects users reviewing at least one same product; 2) U-S-V: it connects users having at least one same star rating within one week; 3) U-V-U: it connects users with top 5% mutual review text similarities (measured by TF-IDF) among all users.

To download the dataset, please visit this Github repo. For any other questions, please email ytongdou(AT)gmail.com for inquiry.

Amazon Berkeley Objects (ABO) is a collection of 147,702 product listings with multilingual metadata and 398,212 unique catalog images. 8,222 listings come with turntable photography (also referred as "spin" or "360º-View" images), as sequences of 24 or 72 images, for a total of 586,584 images in 8,209 unique sequences. For 7,953 products, the collection also provides high-quality 3d models, as glTF 2.0 files.

Amazon Review 2023 is an updated version of the Amazon Review 2018 dataset. This dataset mainly includes reviews (ratings, text) and item metadata (desc- riptions, category information, price, brand, and images). Compared to the pre- vious versions, the 2023 version features larger size, newer reviews (up to Sep 2023), richer and cleaner meta data, and finer-grained timestamps (from day to milli-second).

Amazon Review is a dataset to tackle the task of identifying whether the sentiment of a product review is positive or negative. This dataset includes reviews from four different merchandise categories: Books (B) (2834 samples), DVDs (D) (1199 samples), Electronics (E) (1883 samples), and Kitchen and housewares (K) (1755 samples).

Dataset Card for "amazon-product-data-filter"

  Dataset Summary

The Amazon Product Dataset contains product listing data from the Amazon US website. It can be used for various NLP and classification tasks, such as text generation, product type classification, attribute extraction, image recognition and more.

  Languages

The text in the dataset is in English.

  Dataset Structure





  Data Instances

Each data point provides product information, such… See the full description on the dataset page: https://huggingface.co/datasets/iarbel/amazon-product-data-filter.

Explore our extensive Amazon Product Dataset, featuring detailed information on prices, ratings, sales volume, and more.

Utilize our Amazon reviews dataset for diverse applications to enrich business strategies and market insights. Analyzing this dataset can aid in understanding customer behavior, product performance, and market trends, empowering organizations to refine their product and marketing strategies. Access the entire dataset or tailor a subset to fit your requirements. Popular use cases include: Product Performance Analysis: Analyze Amazon reviews to assess product performance, uncovering customer satisfaction levels, common issues, and highly praised features to inform product improvements and marketing messages. Customer Behavior Insights: Gain insights into customer behavior, purchasing patterns, and preferences, enabling more personalized marketing and product recommendations. Demand Forecasting: Leverage Amazon reviews to predict future product demand by analyzing historical review data and identifying trends, helping to optimize inventory management and sales strategies. Accessing and analyzing the Amazon reviews dataset supports market strategy optimization by leveraging insights to analyze key market trends and customer preferences, enhancing overall business decision-making.

The Amazon-Google dataset for entity resolution derives from the online retailers Amazon.com and the product search service of Google accessible through the Google Base Data API. The dataset contains 1363 entities from amazon.com and 3226 google products as well as a gold standard (perfect mapping) with 1300 matching record pairs between the two data sources. The common attributes between the two data sources are: product name, product description, manufacturer and price.

The dataset was initially published in the repository of the Database Group of the University of Leipzig: https://dbs.uni-leipzig.de/research/projects/object_matching/benchmark_datasets_for_entity_resolution

To enable the reproducibility of the results and the comparability of the performance of different matchers on the Amazon-Google matching task, the dataset was split into fixed train, validation and test sets. The fixed splits are provided in the CompERBench repository:

http://data.dws.informatik.uni-mannheim.de/benchmarkmatchingtasks/index.html

Amazon product questions and their answers, along with the public product information.

Amazon Customer Reviews (a.k.a. Product Reviews) is one of Amazons iconic products. 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. This makes Amazon Customer Reviews a rich source of information for academic researchers in the fields of Natural Language Processing (NLP), Information Retrieval (IR), and Machine Learning (ML), amongst others. Accordingly, we are releasing this data to further research in multiple disciplines related to understanding customer product experiences. Specifically, this dataset was constructed to represent a sample of customer evaluations and opinions, variation in the perception of a product across geographical regions, and promotional intent or bias in reviews.

Over 130+ million customer reviews are available to researchers as part of this release. The data is available in TSV files in the amazon-reviews-pds S3 bucket in AWS US East Region. Each line in the data files corresponds to an individual review (tab delimited, with no quote and escape characters).

Each Dataset contains the following columns:

• marketplace: 2 letter country code of the marketplace where the review was written.
• customer_id: Random identifier that can be used to aggregate reviews written by a single author.
• review_id: The unique ID of the review.
• product_id: The unique Product ID the review pertains to. In the multilingual dataset the reviews for the same product in different countries can be grouped by the same product_id.
• product_parent: Random identifier that can be used to aggregate reviews for the same product.
• product_title: Title of the product.
• product_category: Broad product category that can be used to group reviews (also used to group the dataset into coherent parts).
• star_rating: The 1-5 star rating of the review.
• helpful_votes: Number of helpful votes.
• total_votes: Number of total votes the review received.
• vine: Review was written as part of the Vine program.
• verified_purchase: The review is on a verified purchase.
• review_headline: The title of the review.
• review_body: The review text.
• review_date: The date the review was written.

The 2021 Amazon Last Mile Routing Research Challenge was an innovative research initiative led by Amazon.com and supported by the Massachusetts Institute of Technology’s Center for Transportation and Logistics. Over a period of 4 months, participants were challenged to develop innovative machine learning-based methods to enhance classic optimization-based approaches to solve the travelling salesperson problem, by learning from historical routes executed by Amazon delivery drivers. The primary goal of the Amazon Last Mile Routing Research Challenge was to foster innovative applied research in route planning, building on recent advances in predictive modeling, and using a real-world problem and data. The dataset released for the research challenge includes route-, stop-, and package-level features for 9,184 historical routes performed by Amazon drivers in 2018 in five metropolitan areas in the United States. This real-world dataset excludes any personally identifiable information (all route and package identifiers have been randomly regenerated and related location data have been obfuscated to ensure anonymity). Although multiple synthetic benchmark datasets are available in the literature, the dataset of the 2021 Amazon Last Mile Routing Research Challenge is the first large and publicly available dataset to include instances based on real-world operational routing data. The dataset is fully described and formally introduced in the following Transportation Science article: https://pubsonline.informs.org/doi/10.1287/trsc.2022.1173

Amazon Product Description Dataset

This dataset is a cleaned version of Amazon Product Data. Cleaned by team at https://exnrt.com

421K Unique Examples Empty description rows are being removed. Description Smaller then 200 characters are removed Convert to Proper Format Remove non-ASCII characters from both column And few more techniques

  Original Dataset

This original dataset has 10 Million Examples. Original, Un-cleaned DataSet:… See the full description on the dataset page: https://huggingface.co/datasets/Ateeqq/Amazon-Product-Description.

Get the needed Amazon product review data right from the data extractor! Collect Amazon review information from 19 Amazon countries from the following domains: - amazon.com - amazon.com.au - amazon.com.br - amazon.ca - amazon.cn - amazon.fr - amazon.de - amazon.in - amazon.it - amazon.com.mx - amazon.nl - amazon.sg - amazon.es - amazon.com.tr

Request Ecommerce Product Review dataset by: - keyword - category - seller - product ID (ASIN)

Amazon E-commerce Reviews Data datasets gathered by keyword, seller, category, or ASIN contain: - Product ID (can be extended to the full product information) - Review content and rating - Review metadata

Amazon extraction results can be delivered by schedule or API request, so the data can be extracted in real-time.

DATAANT uses the in-house web scraping service with no concurrency limitations, so unlimited data extractions can be performed simultaneously.

Output can and attributes can be customized to fit your particular needs.

Amazon Bin Image Dataset

The Amazon Bin Image Dataset contains 536,434 images and metadata from bins of a pod in an operating Amazon Fulfillment Center. The bin images in this dataset are captured as robot units carry pods as part of normal Amazon Fulfillment Center operations. This dataset has many images and the corresponding medadata.

The image files have three groups according to its naming scheme.

• A file name with 1~4 digits (1,200): 1.jpg ~ 1200.jpg
• A file name with 5 digits (99,999): 00001.jpg ~ 99999.jpg
• A file name with 6 digits (435,235): 100000.jpg ~ 535234.jpg

Amazon Bin Image Dataset File List dataset aims to provide a CSV file to contain all file locations and the quantity to help the analysis and distributed learning.

Documentation

• https://github.com/awslabs/open-data-docs/tree/main/docs/aft-vbi-pds

Download

• https://registry.opendata.aws/amazon-bin-imagery/
• https://github.com/awslabs/open-data-registry/blob/main/datasets/amazon-bin-imagery.yaml

Context:- Amazon.com, Inc. is an American multinational technology company specializing in e-commerce, cloud computing, digital streaming, and artificial intelligence. Founded by Jeff Bezos in 1994, Amazon has grown into one of the world’s most valuable companies, revolutionizing online retail and cloud services through its Amazon Web Services (AWS) division.

As of March 2025 Amazon has a market cap of $2.249 Trillion USD. This makes Amazon the world's 4th most valuable company by market cap according to our data. The market capitalization, commonly called market cap, is the total market value of a publicly traded company's outstanding shares and is commonly used to measure how much a company is worth.

Content:- This dataset covers Amazon’s daily stock price data from 2000 to 2025. It includes information on: https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F14466026%2F5453b54c1a5488a995b51a5f3b23fd84%2FStock%20dataset%20variables.jpg?generation=1740822549719886&alt=media" alt="">

Time-period: 2000–2025

Acknowlegements This dataset belongs to me.I'm sharing it here for free.You may do with it as you wish.

According to forecasts, net sales of electrical products on Amazon are forecast at over 164 billion U.S. dollars. With a compound annual growth rate of 11.6 percent, this figure is expected to exceed 284 billion dollars by 2026. Yet, the category expected to grow the strongest on the e-commerce platform is health and beauty.