PromptCloud offers cutting-edge data extraction services that empower businesses with real-time, actionable intelligence from the vast expanses of the online marketplace. We are committed to putting data at the heart of your business. Reach out for a no-frills PromptCloud experience- professional, technologically ahead and reliable.

Our Amazon Best Seller Products Dataset is a key tool for businesses looking to understand and capitalize on market trends. It allows you to identify top-selling products and sellers, and track their performance across various categories and subcategories. This dataset is invaluable for competitive intelligence, monitoring trending products, and understanding customer sentiment. It also plays a crucial role in monitoring competitor prices and enhancing product inventory, ensuring that your business stays relevant and competitive.

Beyond Amazon, PromptCloud offers access to a diverse range of Ecommerce Product Data from various e-commerce websites. PromptCloud is a leading provider of advanced web scraping services, uniquely tailored to meet the dynamic needs of modern businesses. Our services are fully customizable, allowing clients to specify source websites, data collection frequencies, data points, and delivery mechanisms to fit their unique requirements​​. The data aggregation feature of our web crawler enables the extraction of data from multiple sources in a single stream, catering to a diverse range of ecommerce clients.

PromptCloud is a leading provider of advanced web scraping services, uniquely tailored to meet the dynamic needs of modern businesses. Our services are fully customizable, allowing clients to specify source websites, data collection frequencies, data points, and delivery mechanisms to fit their unique requirements​​. The data aggregation feature of our web crawler enables the extraction of data from multiple sources in a single stream, catering to a diverse range of clients, from news aggregators to job boards​​.

With over a decade of experience in extracting web data from any e-commerce website, PromptCloud stands as a seasoned veteran in the field. This extensive experience translates into high-quality, reliable data extraction, making PromptCloud your ideal product web data extraction partner. The reliability of our data is uncompromised, with a 100% verification process that ensures accuracy and trustworthiness.

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

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Data includes: Seller ID Seller Business Model: Private Label Seller/Wholesaler Estimated Annual Revenue in $ ( accurate +/-30%) Annual Revenue Bracket [$] % of goods shipped in FBA Amazon Seller Page Seller Website Decision Maker First Name Decision Maker Last Name Direct Email Generic Email Office Phone Number Decision Maker Linkedin URL Seller Name Seller Storefront Link Seller Business Name Seller Business Full Address City State Zip Seller Business Country Number of Reviews [30 days] Number of Reviews [90 days] Number of Reviews [12 months] Number of Reviews [lifetime] Seller Rating Lifetime Total number of products Total number of brands Brands Top Product URL Top Product Shipping From Top Product Category Top Product SubCategory

About SellerDirectories: SellerDirectories.com provides human-researched and verified data on Amazon sellers and eCommerce Brands.

Our human-researched and verified data is trusted by companies like Walmart, Microsoft, Tencent, Helium10, and 250 more! ⭐ Check our verified 5-star reviews on Trustpilot ⭐ https://www.trustpilot.com/review/sellerdirectories.com ⭐

✅ Data Sources: Aggregated from 50+ sources

✅ Data Collection: Human Researched and verified (we don't like scrapped contact data)

✅ 98%+ accurate and up-to-date data (verified)

✅ Brand /seller Targeting Options: Multiple filters available (including revenue, location, business model, and product category)

✅ Customer Service: Lifetime support and accuracy guarantee on your list. Our lists include resources on how best to run outreach campaigns to turn a prospect list into actual business opportunities. Buy B2B contact database with SellerDirectories.com, and get your B2B contacts database sorted!

From 2004 to 2024, the net revenue of Amazon e-commerce and service sales has increased tremendously. In the fiscal year ending December 31, the multinational e-commerce company's net revenue was almost 638 billion U.S. dollars, up from 575 billion U.S. dollars in 2023.Amazon.com, a U.S. e-commerce company originally founded in 1994, is the world’s largest online retailer of books, clothing, electronics, music, and many more goods. As of 2024, the company generates the majority of it's net revenues through online retail product sales, followed by third-party retail seller services, cloud computing services, and retail subscription services including Amazon Prime. From seller to digital environment Through Amazon, consumers are able to purchase goods at a rather discounted price from both small and large companies as well as from other users. Both new and used goods are sold on the website. Due to the wide variety of goods available at prices which often undercut local brick-and-mortar retail offerings, Amazon has dominated the retailer market. As of 2024, Amazon’s brand worth amounts to over 185 billion U.S. dollars, topping the likes of companies such as Walmart, Ikea, as well as digital competitors Alibaba and eBay. One of Amazon's first forays into the world of hardware was its e-reader Kindle, one of the most popular e-book readers worldwide. More recently, Amazon has also released several series of own-branded products and a voice-controlled virtual assistant, Alexa. Headquartered in North America Due to its location, Amazon offers more services in North America than worldwide. As a result, the majority of the company’s net revenue in 2023 was actually earned in the United States, Canada, and Mexico. In 2023, approximately 353 billion U.S. dollars was earned in North America compared to only roughly 131 billion U.S. dollars internationally.

Capture all Amazon product listing details with confidence that you are getting complete and current data. Rainforest API offers comprehensive coverage of each of the product listings or search results in a cleanly structured output.

Rainforest API's advanced parsing means the results returned are exactly what a human user would see. You can request data from any Amazon domain and originate your request from any country in the world. The high-capacity, global infrastructure of the Rainforest API assures you the highest level of performance and reliability. For easy integration with your apps, data is delivered in JSON or CSV format. A convenient CSV Builder allows customization of data columns.

Data is retrieved in real time, by search term, or for single products, by global identifiers such as GTIN, ISBN, UPC and EAN rather than Amazon ASIN. The API automatically performs the ASIN conversion for each request. You can also submit a product page URL (product results), or a category ID (category search results) instead.

So what's in the data from Rainforest API?

Product: - Brand & manufacturer - Manufacturer & Amazon product descriptions - Specifications - Buy Box Winner: price, etc. - 1st party, 2nd party & 3rd party seller data - Additional product details (i.e. energy efficiency, add-ons) - A-Plus content - Imagery - Product videos - Category details (category, bestseller category) - Deals (types, states) - Bundles - Seller offers (including delivery options) - Frequently bought together / Also bought - Also viewed / Similar item to consider - Rating & reviews (incl. full review, top positive, top negative, manufacturer replies) - Stock estimation - Sales estimation (for select Amazon domains)

Search Results: - Product details per search result - Position - Related searches - Related brands

...and more, depending on your request parameters or the search result.

How can Traject Data: Amazon Product Results Data be used? - Product listing management - Price monitoring - Brand protection - Category & product trends monitoring - Market research & competitor intelligence - Location-specific & cross-border Amazon shipping data - Rank tracking on Amazon

Who uses Traject Data: Amazon Product Results Data? This data is leveraged by software developers, marketers, founders, sales & business development teams, researchers, and data analysts & engineers in ecommerce, other retail/wholesale business, agencies and SaaS platforms.

Anyone in your organization who works with your digital presence can develop business intelligence and strategy using this advanced product data.

Merchant API will provide you with all essential data and metrics for conducting comprehensive competitor analysis, price monitoring, and market niche research.

With Google Shopping API you can get:

• Google Shopping Products listed for the specified keyword. The results include product title, description in Google Shopping SERP, product rank, price, reviews, and rating as well as the related domain. • Full detailed Google Shopping Product Specification. You will receive all product attributes and their content from the product specification page. • A list of Google Shopping Sellers of the specified product. The provided data for each seller includes related product base and total price, shipment and purchase details, and special offers. • Google Shopping Sellers Ad URL with all additional parameters set by the seller.

With Amazon API you can get:

• Results from Amazon product listings according to the specified keyword (product name), location, and language parameters. • A list of ASINs (unique product identifiers assigned by Amazon) of all modifications listed for the specified product and information about the product prices based on ASIN • Amazon Choice products

We offer well-rounded API documentation, GUI for API usage control, comprehensive client libraries for different programming languages, free sandbox API testing, ad hoc integration, and deployment support.

We have a pay-as-you-go pricing model. You simply add funds to your account and use them to get data. The account balance doesn't expire.

✔️ Easy-to-handle Excel Sheet ✔️ Human Researched and Verified leads with Direct contacts ✔️ Up to 🇬🇧30K Active Amazon third-party private Label Sellers lead direct contact and info ✔️ Up to 30+ data points for each prospect ✔️ Sort your list by store size, product category, company location, and much more! ✔️ Enjoy a list that has been hand-researched and verified. No scrapped contacts! Buy B2B contact database with SellerDirectories.com, and get your B2B contacts database sorted!

Data includes: Seller ID Seller Business Model: Private Label Seller/Wholesaler Estimated Annual Revenue in $ ( accurate +/-30%) Annual Revenue Bracket [$] % of goods shipped in FBA Amazon Seller Page Seller Website Decision Maker First Name Decision Maker Last Name Direct Email Generic Email Office Phone Number Decision Maker Linkedin URL Seller Name Seller Storefront Link Seller Business Name Seller Business Full Address City State Zip Seller Business Country Number of Reviews [30 days] Number of Reviews [90 days] Number of Reviews [12 months] Number of Reviews [lifetime] Seller Rating Lifetime Total number of products Total number of brands Brands Top Product URL Top Product Shipping From Top Product Category Top Product SubCategory

About SellerDirectories: SellerDirectories.com provides human-researched and verified data on Amazon sellers and eCommerce Brands.

Our human-researched and verified data is trusted by companies like Walmart, Microsoft, Tencent, Helium10, and 250 more! ⭐ Check our verified 5-star reviews on Trustpilot ⭐ https://www.trustpilot.com/review/sellerdirectories.com ⭐

✅ Data Sources: Aggregated from 50+ sources

✅ Data Collection: Human Researched and verified (we don't like scrapped contact data)

✅ 98%+ accurate and up-to-date data (verified)

✅ Brand /seller Targeting Options: Multiple filters available (including revenue, location, business model, and product category)

✅ Customer Service: Lifetime support and accuracy guarantee on your list. Our lists include resources on how best to run outreach campaigns to turn a prospect list into actual business opportunities.

About This Data

This is a list of over 34,000 consumer reviews for Amazon products like the Kindle, Fire TV Stick, and more provided by Datafiniti's Product Database. The dataset includes basic product information, rating, review text, and more for each product.

Note that this is a sample of a large dataset. The full dataset is available through Datafiniti.

What You Can Do With This Data

You can use this data to analyze Amazon’s most successful consumer electronics product launches; discover insights into consumer reviews and assist with machine learning models. E.g.:

• What are the most reviewed Amazon products?
• What are the initial and current number of customer reviews for each product?
• How do the reviews in the first 90 days after a product launch compare to the price of the product?
• How do the reviews in the first 90 days after a product launch compare to the days available for sale?
• Map the keywords in the review text against the review ratings to help train sentiment models.

Data Schema

A full schema for the data is available in our support documentation.

About Datafiniti

Datafiniti provides instant access to web data. We compile data from thousands of websites to create standardized databases of business, product, and property information. Learn more.

Interested in the Full Dataset?

You can access the full dataset by running the following query with Datafiniti’s Product API.

{ "query": "dateUpdated:[2017-09-01 TO *] AND brand:Amazon* AND categories:* AND primaryCategories:* AND name:* AND reviews:*", "format": "csv", "download": true }

**The total number of results may vary.*

Get this data and more by creating a free Datafiniti account or requesting a demo.

✔️ Easy-to-handle Excel Sheet ✔️ Human Researched and Verified leads with Direct contacts ✔️ Up to 🇨🇳 10K Chinese Active Amazon third-party private Label Sellers lead direct contact and info ✔️ Up to 30+ data points for each prospect ✔️ Sort your list by store size, product category, company location, and much more! ✔️ Enjoy a list that has been hand-researched and verified. No scrapped contacts!

Data includes: Seller ID Seller Business Model: Private Label Seller/Wholesaler Estimated Annual Revenue in $ ( accurate +/-30%) Annual Revenue Bracket [$] % of goods shipped in FBA Amazon Seller Page Seller Website Decision Maker First Name Decision Maker Last Name Direct Email Generic Email Office Phone Number Decision Maker Linkedin URL Seller Name Seller Storefront Link Seller Business Name Seller Business Full Address City State Zip Seller Business Country Number of Reviews [30 days] Number of Reviews [90 days] Number of Reviews [12 months] Number of Reviews [lifetime] Seller Rating Lifetime Total number of products Total number of brands Brands Top Product URL Top Product Shipping From Top Product Category Top Product SubCategory

About SellerDirectories: SellerDirectories.com provides human-researched and verified data on Amazon sellers and eCommerce Brands.

Our human-researched and verified data is trusted by companies like Walmart, Microsoft, Tencent, Helium10, and 250 more! ⭐ Check our verified 5-star reviews on Trustpilot ⭐ https://www.trustpilot.com/review/sellerdirectories.com ⭐

✅ Data Sources: Aggregated from 50+ sources

✅ Data Collection: Human Researched and verified (we don't like scrapped contact data)

✅ 98%+ accurate and up-to-date data (verified)

✅ Brand /seller Targeting Options: Multiple filters available (including revenue, location, business model, and product category)

✅ Customer Service: Lifetime support and accuracy guarantee on your list. Our lists include resources on how best to run outreach campaigns to turn a prospect list into actual business opportunities. Buy B2B contact database with SellerDirectories.com, and get your B2B contacts database sorted!

OGBN-Products

Webpage: https://ogb.stanford.edu/docs/nodeprop/#ogbn-products

Usage in Python

import os.path as osp
import pandas as pd
import datatable as dt
import torch
import torch_geometric as pyg
from ogb.nodeproppred import PygNodePropPredDataset

class PygOgbnProducts(PygNodePropPredDataset):
  def _init_(self, meta_csv = None):
    root, name, transform = '/kaggle/input', 'ogbn-products', None
    if meta_csv is None:
      meta_csv = osp.join(root, name, 'ogbn-master.csv')
    master = pd.read_csv(meta_csv, index_col = 0)
    meta_dict = master[name]
    meta_dict['dir_path'] = osp.join(root, name)
    super()._init_(name = name, root = root, transform = transform, meta_dict = meta_dict)
  def get_idx_split(self, split_type = None):
    if split_type is None:
      split_type = self.meta_info['split']
    path = osp.join(self.root, 'split', split_type)
    if osp.isfile(os.path.join(path, 'split_dict.pt')):
      return torch.load(os.path.join(path, 'split_dict.pt'))
    if self.is_hetero:
      train_idx_dict, valid_idx_dict, test_idx_dict = read_nodesplitidx_split_hetero(path)
      for nodetype in train_idx_dict.keys():
        train_idx_dict[nodetype] = torch.from_numpy(train_idx_dict[nodetype]).to(torch.long)
        valid_idx_dict[nodetype] = torch.from_numpy(valid_idx_dict[nodetype]).to(torch.long)
        test_idx_dict[nodetype] = torch.from_numpy(test_idx_dict[nodetype]).to(torch.long)
        return {'train': train_idx_dict, 'valid': valid_idx_dict, 'test': test_idx_dict}
    else:
      train_idx = dt.fread(osp.join(path, 'train.csv'), header = None).to_numpy().T[0]
      train_idx = torch.from_numpy(train_idx).to(torch.long)
      valid_idx = dt.fread(osp.join(path, 'valid.csv'), header = None).to_numpy().T[0]
      valid_idx = torch.from_numpy(valid_idx).to(torch.long)
      test_idx = dt.fread(osp.join(path, 'test.csv'), header = None).to_numpy().T[0]
      test_idx = torch.from_numpy(test_idx).to(torch.long)
      return {'train': train_idx, 'valid': valid_idx, 'test': test_idx}

dataset = PygOgbnProducts()
split_idx = dataset.get_idx_split()
train_idx, valid_idx, test_idx = split_idx['train'], split_idx['valid'], split_idx['test']
graph = dataset[0] # PyG Graph object

Description

Graph: The ogbn-products dataset is an undirected and unweighted graph, representing an Amazon product co-purchasing network [1]. Nodes represent products sold in Amazon, and edges between two products indicate that the products are purchased together. The authors follow [2] to process node features and target categories. Specifically, node features are generated by extracting bag-of-words features from the product descriptions followed by a Principal Component Analysis to reduce the dimension to 100.

Prediction task: The task is to predict the category of a product in a multi-class classification setup, where the 47 top-level categories are used for target labels.

Dataset splitting: The authors consider a more challenging and realistic dataset splitting that differs from the one used in [2] Instead of randomly assigning 90% of the nodes for training and 10% of the nodes for testing (without use of a validation set), use the sales ranking (popularity) to split nodes into training/validation/test sets. Specifically, the authors sort the products according to their sales ranking and use the top 8% for training, next top 2% for validation, and the rest for testing. This is a more challenging splitting procedure that closely matches the real-world application where labels are first assigned to important nodes in the network and ML models are subsequently used to make predictions on less important ones.

Note 1: A very small number of self-connecting edges are repeated (see here); you may remove them if necessary.

Note 2: For undirected graphs, the loaded graphs will have the doubled number of edges because the bidirectional edges will be added automatically.

Summary

Open Graph Benchmark

Website: https://ogb.stanford.edu

The Open Graph Benchmark (OGB) [3] is a collection of realistic, large-scale, and diverse benchmark datasets for machine learning on graphs. OGB datasets are automatically downloaded, processed, and split using the OGB Data Loader. The model performance can be evaluated using the OGB Evaluator in a unified manner.

References

[1] http://manikvarma.org/downloads/XC/XMLRepository.html [2] Wei-Lin Chiang, Xuanqing Liu, Si Si, Yang Li, Samy Bengio, and Cho-Jui Hsieh. Cluster-GCN: An efficient algorithm for training deep and large graph convolutional networks. ACM SIGKDD Conference on Knowledge Discovery and Data Mining, pp. 257–266, 2019. [3] Weihua Hu, Matthias Fey, Marinka Zitnik, Yuxiao Dong, Hongyu Ren, Bowen Liu, Michele Catasta, and Jure Leskovec. Open graph benchmark: Datasets for machine learning on graphs. Advances in Neural Information Processing Systems, pp. 22118–22133, 2020.

License: Amazon License

By accessing the Amazon Customer Reviews Library ("Reviews Library"), you agree that the Reviews Library is an Amazon Service subject to the Amazon.com Conditions of Use (https://www.amazon.com/gp/help/customer/display.html/ref=footer_cou?ie=UTF8&nodeId=508088) and you agree to be bound by them, with the following additional conditions: In addition to the license rights granted under the Conditions of Use, Amazon or its content providers grant you a limited, non-exclusive, non-transferable, non-sublicensable, revocable license to access and use the Reviews Library for purposes of academic research. You may not resell, republish, or make any commercial use of the Reviews Library or its contents, including use of the Reviews Library for commercial research, such as research related to a funding or consultancy contract, internship, or other relationship in which the results are provided for a fee or delivered to a for-profit organization. You may not (a) link or associate content in the Reviews Library with any personal information (including Amazon customer accounts), or (b) attempt to determine the identity of the author of any content in the Reviews Library. If you violate any of the foregoing conditions, your license to access and use the Reviews Library will automatically terminate without prejudice to any of the other rights or remedies Amazon may have.

Disclaimer

I am NOT the author of this dataset. It was downloaded from its official website. I assume no responsibility or liability for the content in this dataset. Any questions, problems or issues, please contact the original authors at their website or their GitHub repo.

MuMu is a Multimodal Music dataset with multi-label genre annotations that combines information from the Amazon Reviews dataset and the Million Song Dataset (MSD). The former contains millions of album customer reviews and album metadata gathered from Amazon.com. The latter is a collection of metadata and precomputed audio features for a million songs.

To map the information from both datasets we use MusicBrainz. This process yields the final set of 147,295 songs, which belong to 31,471 albums. For the mapped set of albums, there are 447,583 customer reviews from the Amazon Dataset. The dataset have been used for multi-label music genre classification experiments in the related publication. In addition to genre annotations, this dataset provides further information about each album, such as genre annotations, average rating, selling rank, similar products, and cover image url. For every text review it also provides helpfulness score of the reviews, average rating, and summary of the review.

The mapping between the three datasets (Amazon, MusicBrainz and MSD), genre annotations, metadata, data splits, text reviews and links to images are available here. Images and audio files can not be released due to copyright issues.

MuMu dataset (mapping, metadata, annotations and text reviews)

Data splits and multimodal feature embeddings for ISMIR multi-label classification experiments

These data can be used together with the Tartarus deep learning library https://github.com/sergiooramas/tartarus.

NOTE: This version provides simplified files with metadata and splits.

Scientific References

Please cite the following papers if using MuMu dataset or Tartarus library.

Oramas, S., Barbieri, F., Nieto, O., and Serra, X (2018). Multimodal Deep Learning for Music Genre Classification, Transactions of the International Society for Music Information Retrieval, V(1).

Oramas S., Nieto O., Barbieri F., & Serra X. (2017). Multi-label Music Genre Classification from audio, text and images using Deep Features. In Proceedings of the 18th International Society for Music Information Retrieval Conference (ISMIR 2017). https://arxiv.org/abs/1707.04916

These datasets contain reviews from the Steam video game platform, and information about which games were bundled together.

Metadata includes

• reviews

• purchases, plays, recommends (likes)

• product bundles

• pricing information

Basic Statistics:

• Reviews: 7,793,069

• Users: 2,567,538

• Items: 15,474

• Bundles: 615

✔️ Easy-to-handle Excel Sheet ✔️ Human Researched and Verified leads with Direct contacts ✔️ Up to 200K Active Amazon third-party private Label Sellers lead direct contact and info ✔️ Up to 30+ data points for each prospect ✔️ Sort your list by store size, product category, company location, and much more! ✔️ Enjoy a list that has been hand-researched and verified. No scrapped contacts! Buy a B2B contact database with SellerDirectories.com, and get your B2B contacts database sorted!

Data includes: Seller ID Seller Business Model: Private Label Seller/Wholesaler Estimated Annual Revenue in $ ( accurate +/-30%) Annual Revenue Bracket [$] % of goods shipped in FBA Amazon Seller Page Seller Website Decision Maker First Name Decision Maker Last Name Direct Email Generic Email Office Phone Number Decision Maker Linkedin URL Seller Name Seller Storefront Link Seller Business Name Seller Business Full Address City State Zip Seller Business Country Number of Reviews [30 days] Number of Reviews [90 days] Number of Reviews [12 months] Number of Reviews [lifetime] Seller Rating Lifetime Total number of products Total number of brands Brands Top Product URL Top Product Shipping From Top Product Category Top Product SubCategory

About SellerDirectories: SellerDirectories.com provides human-researched and verified data on Amazon sellers and eCommerce Brands.

Our human-researched and verified data is trusted by companies like Walmart, Microsoft, Tencent, Helium10, and 250 more! ⭐ Check our verified 5-star reviews on Trustpilot ⭐ https://www.trustpilot.com/review/sellerdirectories.com ⭐

✅ Data Sources: Aggregated from 50+ sources

✅ Data Collection: Human Researched and verified (we don't like scrapped contact data)

✅ 98%+ accurate and up-to-date data (verified)

✅ Brand /seller Targeting Options: Multiple filters available (including revenue, location, business model, and product category)

✅ Customer Service: Lifetime support and accuracy guarantee on your list. Our lists include resources on how best to run outreach campaigns to turn a prospect list into actual business opportunities.

Global MODIS vegetation indices are designed to provide consistent spatial and temporal comparisons of vegetation conditions. Blue, red, and near-infrared reflectances, centered at 469-nanometers, 645-nanometers, and 858-nanometers, respectively, are used to determine the MODIS daily vegetation indices. The MODIS Normalized Difference Vegetation Index (NDVI) complements NOAA's Advanced Very High Resolution Radiometer (AVHRR) NDVI products and provides continuity for time series historical applications. MODIS also includes a new Enhanced Vegetation Index (EVI) that minimizes canopy background variations and maintains sensitivity over dense vegetation conditions. The EVI also uses the blue band to remove residual atmosphere contamination caused by smoke and sub-pixel thin cloud clouds. The MODIS NDVI and EVI products are computed from atmospherically corrected bi-directional surface reflectances that have been masked for water, clouds, heavy aerosols, and cloud shadows. Global MOD13Q1 data are provided every 16 days at 250-meter spatial resolution as a gridded level-3 product in the Sinusoidal projection. Lacking a 250m blue band, the EVI algorithm uses the 500m blue band to correct for residual atmospheric effects, with negligible spatial artifacts. Vegetation indices are used for global monitoring of vegetation conditions and are used in products displaying land cover and land cover changes. These data may be used as input for modeling global biogeochemical and hydrologic processes and global and regional climate. These data also may be used for characterizing land surface biophysical properties and processes, including primary production and land cover conversion.

Unlock powerful insights with the Amazon Fine Food dataset, offering access to millions of records from any Amazon domain. This dataset provides comprehensive data points such as product titles, descriptions, brand details, pricing (initial and discounted), availability, customer ratings, reviews, and product categories. Additionally, it includes unique identifiers like ASINs, ingredients, and seller information, allowing you to analyze food listings, trends, and customer preferences with precision. Use this dataset to optimize your eCommerce strategies by benchmarking competitor pricing, identifying top-performing brands, and tracking customer sentiment through reviews and ratings. Gain valuable insights into consumer demand, dietary preferences, and market gaps to make data-driven decisions that enhance your inventory management, marketing campaigns, and product strategies. Whether you’re a retailer, marketer, data analyst, or researcher, the Amazon Fine Food dataset empowers you with the data needed to stay competitive in the dynamic eCommerce landscape. Available in various formats such as JSON, CSV, and Parquet, and delivered via flexible options like API, S3, or email, this dataset ensures seamless integration into your workflows.

✔️ Easy-to-handle Excel Sheet ✔️ Human Researched and Verified leads with Direct contacts ✔️ Up to 🇫🇷10K French Active Amazon third-party private Label Sellers lead direct contact and info ✔️ Up to 30+ data points for each prospect ✔️ Sort your list by store size, product category, company location, and much more! ✔️ Enjoy a list that has been hand-researched and verified. No scrapped contacts! Buy B2B contact database with SellerDirectories.com, and get your B2B contacts database sorted!

Data includes: Seller ID Seller Business Model: Private Label Seller/Wholesaler Estimated Annual Revenue in $ ( accurate +/-30%) Annual Revenue Bracket [$] % of goods shipped in FBA Amazon Seller Page Seller Website Decision Maker First Name Decision Maker Last Name Direct Email Generic Email Office Phone Number Decision Maker Linkedin URL Seller Name Seller Storefront Link Seller Business Name Seller Business Full Address City State Zip Seller Business Country Number of Reviews [30 days] Number of Reviews [90 days] Number of Reviews [12 months] Number of Reviews [lifetime] Seller Rating Lifetime Total number of products Total number of brands Brands Top Product URL Top Product Shipping From Top Product Category Top Product SubCategory

About SellerDirectories: SellerDirectories.com provides human-researched and verified data on Amazon sellers and eCommerce Brands.

Our human-researched and verified data is trusted by companies like Walmart, Microsoft, Tencent, Helium10, and 250 more! ⭐ Check our verified 5-star reviews on Trustpilot ⭐ https://www.trustpilot.com/review/sellerdirectories.com ⭐

✅ Data Sources: Aggregated from 50+ sources

✅ Data Collection: Human Researched and verified (we don't like scrapped contact data)

✅ 98%+ accurate and up-to-date data (verified)

✅ Brand /seller Targeting Options: Multiple filters available (including revenue, location, business model, and product category)

✅ Customer Service: Lifetime support and accuracy guarantee on your list. Our lists include resources on how best to run outreach campaigns to turn a prospect list into actual business opportunities.

Over 2 million sellers of 10 European Amazon marketplaces: (Italy, France, Spain, Germany, Belgium, Greece, Czech, Poland, Croatia and the United Kingdom.

• The data can be customized to your filtering criteria and needs.

Fields available: • Seller URL • Seller ID • Store Name • Main Category • Brand owner • FBA Seller • Experience on Amazon • Orders • Business Name • Full Business Address • Country • State/Province • ZIP Code • City • Owner first name • Owner last name • Owner email • Email - company • Website • Star rating • Percentage of positive reviews • Lifetime number of seller ratings • Number of products listed • Brands listed • Number of brands listed

Pricing (no order minimums): • <500 leads: $0.59 per lead • 501-5000 leads: $0.39 per lead • 5000+ leads: $0.29 per lead

We update our seller database every month to keep the data current.

Context

This dataset consists of reviews of fine foods from amazon. The data span a period of more than 10 years, including all ~500,000 reviews up to October 2012. Reviews include product and user information, ratings, and a plain text review. It also includes reviews from all other Amazon categories.

Contents

• Reviews.csv: Pulled from the corresponding SQLite table named Reviews in database.sqlite
  
• database.sqlite: Contains the table 'Reviews'
  
  

Data includes:
- Reviews from Oct 1999 - Oct 2012
- 568,454 reviews
- 256,059 users
- 74,258 products
- 260 users with > 50 reviews

Acknowledgements

See this SQLite query for a quick sample of the dataset.

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

• J. McAuley and J. Leskovec. From amateurs to connoisseurs: modeling the evolution of user expertise through online reviews. WWW, 2013.

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According to Cognitive Market Research, the global Ai Training Data market size is USD 1865.2 million in 2023 and will expand at a compound annual growth rate (CAGR) of 23.50% from 2023 to 2030.

The demand for Ai Training Data is rising due to the rising demand for labelled data and diversification of AI applications.
Demand for Image/Video remains higher in the Ai Training Data market.
The Healthcare category held the highest Ai Training Data market revenue share in 2023.
North American Ai Training Data will continue to lead, whereas the Asia-Pacific Ai Training Data market will experience the most substantial growth until 2030.

Market Dynamics of AI Training Data Market

Key Drivers of AI Training Data Market

Rising Demand for Industry-Specific Datasets to Provide Viable Market Output

A key driver in the AI Training Data market is the escalating demand for industry-specific datasets. As businesses across sectors increasingly adopt AI applications, the need for highly specialized and domain-specific training data becomes critical. Industries such as healthcare, finance, and automotive require datasets that reflect the nuances and complexities unique to their domains. This demand fuels the growth of providers offering curated datasets tailored to specific industries, ensuring that AI models are trained with relevant and representative data, leading to enhanced performance and accuracy in diverse applications.

In July 2021, Amazon and Hugging Face, a provider of open-source natural language processing (NLP) technologies, have collaborated. The objective of this partnership was to accelerate the deployment of sophisticated NLP capabilities while making it easier for businesses to use cutting-edge machine-learning models. Following this partnership, Hugging Face will suggest Amazon Web Services as a cloud service provider for its clients.

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Advancements in Data Labelling Technologies to Propel Market Growth

The continuous advancements in data labelling technologies serve as another significant driver for the AI Training Data market. Efficient and accurate labelling is essential for training robust AI models. Innovations in automated and semi-automated labelling tools, leveraging techniques like computer vision and natural language processing, streamline the data annotation process. These technologies not only improve the speed and scalability of dataset preparation but also contribute to the overall quality and consistency of labelled data. The adoption of advanced labelling solutions addresses industry challenges related to data annotation, driving the market forward amidst the increasing demand for high-quality training data.

In June 2021, Scale AI and MIT Media Lab, a Massachusetts Institute of Technology research centre, began working together. To help doctors treat patients more effectively, this cooperation attempted to utilize ML in healthcare.

www.ncbi.nlm.nih.gov/pmc/articles/PMC7325854/

Restraint Factors Of AI Training Data Market

Data Privacy and Security Concerns to Restrict Market Growth

A significant restraint in the AI Training Data market is the growing concern over data privacy and security. As the demand for diverse and expansive datasets rises, so does the need for sensitive information. However, the collection and utilization of personal or proprietary data raise ethical and privacy issues. Companies and data providers face challenges in ensuring compliance with regulations and safeguarding against unauthorized access or misuse of sensitive information. Addressing these concerns becomes imperative to gain user trust and navigate the evolving landscape of data protection laws, which, in turn, poses a restraint on the smooth progression of the AI Training Data market.

How did COVID–19 impact the Ai Training Data market?

The COVID-19 pandemic has had a multifaceted impact on the AI Training Data market. While the demand for AI solutions has accelerated across industries, the availability and collection of training data faced challenges. The pandemic disrupted traditional data collection methods, leading to a slowdown in the generation of labeled datasets due to restrictions on physical operations. Simultaneously, the surge in remote work and the increased reliance on AI-driven technologies for various applications fueled the need for diverse and relevant training data. This duali...