OpenWeb Ninja's Amazon Data API provides fast, reliable, and real-time access to Amazon Data and on all 22 Amazon domains / countries.

The OpenWeb Ninja's Amazon Data API covers over 300 million Product Listings Data (products, books, media, wine, and services) and provides over 40 data points per product.

OpenWeb Ninja's Amazon Data common use cases: - Price Optimization & Price Comparison - Market Research - Competitive Analysis - Product Research & Trend Analysis - Customer Reviews Analysis

OpenWeb Ninja's Amazon Data Stats & Capabilities: - 40+ data points per job listing - 300M+ Product Listings - 22 Amazon countries/domains supported - Real-time Amazon product data, including offers, and deals (Today's Deals) - Detailed product reviews and ratings data - Amazon Best Sellers across multiple Amazon categories - Conversion from Amazon ASIN to GTIN/EAN/ISBN

*Also find Metacritic Movies and Metacritic TV Shows datasets.*

Metacritic Games and Reviews Dataset

This dataset contains a collection of video games and their corresponding reviews from Metacritic, a popular aggregate review site. The data provides insights into various video games across different platforms, including PC, PlayStation, Xbox, and others. Each game entry includes critical reviews, user reviews, ratings, and other relevant information that can be used for analysis, natural language processing, machine learning, and predictive modeling.

Important Note: *The games in this collection are selected from Metacritic's Best Games of All Time list, which only includes titles that have received at least 7 reviews, ensuring a minimum level of critical and user input.*

Up-to-dateness: *This dataset is accurate as of March 14, 2025, and includes the most current rankings and game details available at that time.*

Content

The dataset contains general information and scores of 13K+ games and their corresponding 1.6M+ user/critic reviews collected by sending automated requests to Metacritic's public backend API using Python's requests and pandas libraries.

Potential Uses

• Sentiment analysis and natural language processing on reviews.
• Predictive modeling for predicting user ratings based on features like genre, publisher, and developer.
• Data analysis on trends in game quality, genres, or platform performance.
• Comparing critical reviews and user reviews to understand the divergence in ratings.

This dataset is perfect for researchers, game enthusiasts, and data scientists who are interested in exploring the gaming industry through data analysis.

Acknowledgements

• The datasets are scraped from metacritic.com
  
• The code for scraping the dataset can be found on my GitHub repository

This dataset provides comprehensive real-time data from Amazon's global marketplaces. It includes detailed product information, reviews, seller profiles, best sellers, deals, influencers, and more across all Amazon domains worldwide. The data covers product attributes like pricing, availability, specifications, reviews and ratings, as well as seller information including profiles, contact details, and performance metrics. Users can leverage this dataset for price monitoring, competitive analysis, market research, and building e-commerce applications. The API enables real-time access to Amazon's vast product catalog and marketplace data, helping businesses make data-driven decisions about pricing, inventory, and market positioning. Whether you're conducting market analysis, tracking competitors, or building e-commerce tools, this dataset provides current and reliable Amazon marketplace data. The dataset is delivered in a JSON format via REST API.

Best virtual data rooms 2024 dataset is created to provide the data room users and M&A specialists with detailed information on the best virtual data rooms. The dataset contains the descriptions of each dataroom solution and their ratings.

Fast and Reliable real-time API access to Amazon Data with 300M+ Product Listings, including extensive Product Details, Product Reviews, All Product Offers, Amazon Best Sellers, Deals, ASIN to GTIN/EAN conversion, and more.

The Easiest Way to Collect Data from the Internet Download anything you see on the internet into spreadsheets within a few clicks using our ready-made web crawlers or a few lines of code using our APIs

We have made it as simple as possible to collect data from websites

Easy to Use Crawlers Amazon Product Details and Pricing Scraper Amazon Product Details and Pricing Scraper Get product information, pricing, FBA, best seller rank, and much more from Amazon.

Google Maps Search Results Google Maps Search Results Get details like place name, phone number, address, website, ratings, and open hours from Google Maps or Google Places search results.

Twitter Scraper Twitter Scraper Get tweets, Twitter handle, content, number of replies, number of retweets, and more. All you need to provide is a URL to a profile, hashtag, or an advance search URL from Twitter.

Amazon Product Reviews and Ratings Amazon Product Reviews and Ratings Get customer reviews for any product on Amazon and get details like product name, brand, reviews and ratings, and more from Amazon.

Google Reviews Scraper Google Reviews Scraper Scrape Google reviews and get details like business or location name, address, review, ratings, and more for business and places.

Walmart Product Details & Pricing Walmart Product Details & Pricing Get the product name, pricing, number of ratings, reviews, product images, URL other product-related data from Walmart.

Amazon Search Results Scraper Amazon Search Results Scraper Get product search rank, pricing, availability, best seller rank, and much more from Amazon.

Amazon Best Sellers Amazon Best Sellers Get the bestseller rank, product name, pricing, number of ratings, rating, product images, and more from any Amazon Bestseller List.

Google Search Scraper Google Search Scraper Scrape Google search results and get details like search rank, paid and organic results, knowledge graph, related search results, and more.

Walmart Product Reviews & Ratings Walmart Product Reviews & Ratings Get customer reviews for any product on Walmart.com and get details like product name, brand, reviews, and ratings.

Scrape Emails and Contact Details Scrape Emails and Contact Details Get emails, addresses, contact numbers, social media links from any website.

Walmart Search Results Scraper Walmart Search Results Scraper Get Product details such as pricing, availability, reviews, ratings, and more from Walmart search results and categories.

Glassdoor Job Listings Glassdoor Job Listings Scrape job details such as job title, salary, job description, location, company name, number of reviews, and ratings from Glassdoor.

Indeed Job Listings Indeed Job Listings Scrape job details such as job title, salary, job description, location, company name, number of reviews, and ratings from Indeed.

LinkedIn Jobs Scraper Premium LinkedIn Jobs Scraper Scrape job listings on LinkedIn and extract job details such as job title, job description, location, company name, number of reviews, and more.

Redfin Scraper Premium Redfin Scraper Scrape real estate listings from Redfin. Extract property details such as address, price, mortgage, redfin estimate, broker name and more.

Yelp Business Details Scraper Yelp Business Details Scraper Scrape business details from Yelp such as phone number, address, website, and more from Yelp search and business details page.

Zillow Scraper Premium Zillow Scraper Scrape real estate listings from Zillow. Extract property details such as address, price, Broker, broker name and more.

Amazon product offers and third party sellers Amazon product offers and third party sellers Get product pricing, delivery details, FBA, seller details, and much more from the Amazon offer listing page.

Realtor Scraper Premium Realtor Scraper Scrape real estate listings from Realtor.com. Extract property details such as Address, Price, Area, Broker and more.

Target Product Details & Pricing Target Product Details & Pricing Get product details from search results and category pages such as pricing, availability, rating, reviews, and 20+ data points from Target.

Trulia Scraper Premium Trulia Scraper Scrape real estate listings from Trulia. Extract property details such as Address, Price, Area, Mortgage and more.

Amazon Customer FAQs Amazon Customer FAQs Get FAQs for any product on Amazon and get details like the question, answer, answered user name, and more.

Yellow Pages Scraper Yellow Pages Scraper Get details like business name, phone number, address, website, ratings, and more from Yellow Pages search results.

ABSTRACT The exponential increase of published data and the diversity of systems require the adoption of good practices to achieve quality indexes that enable discovery, access, and reuse. To identify good practices, an integrative review was used, as well as procedures from the ProKnow-C methodology. After applying the ProKnow-C procedures to the documents retrieved from the Web of Science, Scopus and Library, Information Science & Technology Abstracts databases, an analysis of 31 items was performed. This analysis allowed observing that in the last 20 years the guidelines for publishing open government data had a great impact on the Linked Data model implementation in several domains and currently the FAIR principles and the Data on the Web Best Practices are the most highlighted in the literature. These guidelines presents orientations in relation to various aspects for the publication of data in order to contribute to the optimization of quality, independent of the context in which they are applied. The CARE and FACT principles, on the other hand, although they were not formulated with the same objective as FAIR and the Best Practices, represent great challenges for information and technology scientists regarding ethics, responsibility, confidentiality, impartiality, security, and transparency of data.

Access a wealth of entertainment data with our OTT (Over-the-Top) scraping service. Gather music, movie, and IMDB reviews & ratings data effortlessly. Extract Data from popular platforms like Netflix, Hulu, Spotify, IMDb, and more

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.

Rotten Tomatoes Top Movie Ratings

Rankings, Reviews and Genres

By Prasert Kanawattanachai [source]

About this dataset

This dataset includes the top movies according to Rotten Tomatoes rankings in all genres (such as Comedy, Drama, Romance, & Action). Each movie listing is accompanied with its rank within the listing and its rating on the Tomatometer, representing viewers' and critics' top-rated selections. It contains all the information you need to stay up to date on what’s currently popular in the movie world – from Star Wars to The Farewell - and make informed decisions when watching a flick. So don’t let reading reviews weigh you down! Let these ratings guide you towards finding your perfect film

More Datasets

For more datasets, click here.

Featured Notebooks

• 🚨 Your notebook can be here! 🚨!

How to use the dataset

This dataset contains information on the top movies rated by Rotten Tomatoes. It includes columns like rank, title, rating (on a scale of 0-100), number of reviews, and genre. With this dataset you can compare movie ratings across different genres and see how they compare.

Research Ideas

• Analyzing the year's most successful movie genres to improve movie production
• Identifying consumer tastes in movies and predicting successes of upcoming releases
• Uncovering correlations between movie ratings and number of reviews

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

Unknown License - Please check the dataset description for more information.

Columns

File: rotten_tomatoes_top_movies_2019-01-15.csv | Column name | Description | |:----------------------|:--------------------------------------------------------| | Rank | The ranking of the movie on the list. (Integer) | | Title | The title of the movie. (String) | | RatingTomatometer | The rating of the movie on Rotten Tomatoes. (Integer) | | No. of Reviews | The number of reviews the movie has received. (Integer) | | Genres | The genre classification of the movie. (String) |

Acknowledgements

If you use this dataset in your research, please credit the original authors. If you use this dataset in your research, please credit Prasert Kanawattanachai.

Use our Best Buy products to collect ratings, prices, and descriptions about products from an e-commerce online web. You can purchase either the entire dataset or a customized subset, depending on your requirements. The Best Buy Products Dataset stands as a comprehensive resource for businesses, researchers, and analysts aiming to navigate the vast array of products offered by Best Buy, a leading retailer in consumer electronics and technology. Tailored to provide a deep understanding of Best Buy's e-commerce ecosystem, this dataset facilitates market analysis, pricing optimization, customer behavior comprehension, and competitor assessment. At its core, the dataset encompasses essential attributes such as product ID, title, descriptions, ratings, reviews, pricing details, and seller information. These fundamental data elements empower users to glean insights into product performance, customer sentiment, and seller credibility, thereby facilitating informed decision-making processes. Whether you're a retailer looking to enhance your product portfolio, a researcher investigating trends in consumer electronics, or an analyst seeking to refine e-commerce strategies, the Best Buy Products Dataset offers a valuable resource for uncovering opportunities and driving success in the ever-evolving landscape of retail.

The statistic shows the leading benefits of business intelligence and data analytics to business organizations in the United States, according to a 2015 survey conducted by the Harvard Business Review Analytics Service. As of 2015, ** percent of respondents felt that the ability to access data any time, anywhere was an important benefit of business intelligence and data analytics to their organization.

Analysis of ‘Amazon Product Reviews Dataset’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/yamqwe/amazon-product-reviews-datasete on 13 February 2022.

--- Dataset description provided by original source is as follows ---

About this dataset

This dataset contains 30K records of product reviews from amazon.com.

This dataset was created by PromptCloud and DataStock

Content

This dataset contains the following:

• Total Records Count: 43729

• Domain Name: amazon.com

• Date Range: 01st Jan 2020 - 31st Mar 2020

• File Extension: CSV

• Available Fields:
  -- Uniq Id,
  -- Crawl Timestamp,
  -- Billing Uniq Id,
  -- Rating,
  -- Review Title,
  -- Review Rating,
  -- Review Date,
  -- User Id,
  -- Brand,
  -- Category,
  -- Sub Category,
  -- Product Description,
  -- Asin,
  -- Url,
  -- Review Content,
  -- Verified Purchase,
  -- Helpful Review Count,
  -- Manufacturer Response

Acknowledgements

We wouldn't be here without the help of our in house teams at PromptCloud and DataStock. Who has put their heart and soul into this project like all other projects? We want to provide the best quality data and we will continue to do so.

Inspiration

The inspiration for these datasets came from research. Reviews are something that is important wit everybody across the globe. So we decided to come up with this dataset that shows us exactly how the user reviews help companies to better their products.

This dataset was created by PromptCloud and contains around 0 samples along with Billing Uniq Id, Verified Purchase, technical information and other features such as: - Crawl Timestamp - Manufacturer Response - and more.

How to use this dataset

• Analyze Helpful Review Count in relation to Sub Category
• Study the influence of Review Date on Product Description
• More datasets

Acknowledgements

If you use this dataset in your research, please credit PromptCloud

Start A New Notebook!

--- Original source retains full ownership of the source dataset ---

Use the OpenWeb Ninja Google Play App Store Data API to access comprehensive data on Google Play Store, including Android Apps / Games, reviews, top charts, search, and more. Our extensive dataset provides over 40 app store data points, enabling you to gain deep insights into the market.

The App Store Data dataset includes all key app details:

App Name, Description, Rating, Photos, Downloads, Version Information, App Size, Permissions, Developer and Contact Information, Consumer Review Data.

Altosight | AI-Powered Amazon Data, eBay Data & More | Global Marketplace Insights

✦ Altosight offers robust, AI-powered Amazon Data services that provide deep insights into product listings, reviews, prices, and sales trends.

✦ Amazon Reviews Data, eBay Data, Alibaba Data, and AliExpress Data are also covered, giving businesses the tools they need to make data-driven decisions across the world’s largest marketplaces.

Our Amazon Data encompasses a broad range of publicly available information from Amazon’s marketplace, which can be used to improve customer experience, personalize recommendations, optimize operations, and drive business success.

With unlimited free data points, fast delivery, and no setup costs, Altosight provides unparalleled flexibility and efficiency.

➤ We offer multiple data delivery options including API, CSV, JSON, and FTP, ensuring seamless integration into your business processes at no additional charge.

― Key Use Cases ―

➤ Marketplace Expansion & Product Assortment Optimization

🔹 Identify gaps in your product offerings by comparing competitor inventories with Alibaba Data, Amazon Data, and eBay Data.

🔹 Expand your product catalog by analyzing trends in best-sellers, emerging products, and market demand.

🔹 Use Digital Shelf Data to track product placements, best-seller rankings, and availability across major marketplaces to optimize your digital shelf space.

➤ Customer Sentiment & Product Review Analysis

🔹 Leverage Amazon Reviews Data to understand customer feedback, identify common complaints, and highlight product strengths.

🔹 Analyze AliExpress Data to track seller ratings and customer reviews, providing insights into consumer sentiment across different marketplaces.

🔹 Use these insights to refine product offerings, improve customer satisfaction, and enhance your brand’s reputation.

➤ Competitive Price Monitoring & Dynamic Repricing

🔹 Track product prices across Amazon, eBay, Alibaba, and AliExpress to ensure you remain competitive in the marketplace.

🔹 Use Amazon Data and eBay Data for real-time insights into competitor pricing and discounts.

🔹 Implement dynamic repricing strategies to react to price changes in real-time, ensuring your products always stay competitively priced.

➤ Product Sourcing & Wholesaler Opportunities

🔹 Use Alibaba Data and AliExpress Data to uncover new product opportunities and identify potential wholesalers.

🔹 Discover trending products to source for your business and form partnerships with reliable suppliers, streamlining your supply chain and business growth.

➤ Market Trend Identification & Forecasting

🔹 Use Alibaba Data and AliExpress Data to identify emerging trends in consumer behavior, product categories, and price fluctuations.

🔹 Conduct comprehensive market research to forecast product demand and industry trends based on historical data from Amazon and other marketplaces.

🔹 Stay ahead of market changes by leveraging real-time data for strategic decision-making, product launches, and marketing initiatives.

➤ Retailer & Brand Performance Tracking

🔹 Track the performance of specific retailers or brands across Amazon, eBay, Alibaba, and AliExpress using detailed sales and review data.

🔹 Monitor how frequently products move up or down in rankings, providing valuable insights for brand positioning and marketing effectiveness.

🔹 Analyze which retailers sell particular brands and products, helping businesses identify new partnerships or distribution opportunities.

― Data Collection & Quality ―

✔ Publicly Sourced Data: Altosight collects Amazon Data, Amazon Reviews Data, eBay Data, Alibaba Data, and AliExpress Data from publicly available sources. This includes product information, transaction data, reviews, and other valuable data points that are essential for making informed business decisions.

✔ AI-Powered Scraping: Our AI-driven technology handles CAPTCHAs, dynamic content, and JavaScript-heavy websites to ensure continuous and accurate data collection. We extract and structure Amazon Reviews Data, Digital Shelf Data, and other relevant marketplace data for easy integration into your existing systems.

✔ High-Quality Data: Altosight ensures all data is cleaned, structured, and ready for use, with high accuracy and reliability. Our solutions are ideal for market research, competitor analysis, and operational optimization.

― Why Choose Altosight? ―

✔ Unlimited Data Points: Altosight offers unlimited free data points, allowing you to extract as many product attributes or sales data as needed without additional charges. This ensures cost-effectiveness while maintaining access to all the insights you require.

✔ Proprietary Anti-Blocking Technology: Our proprietary scraping technology ensures continuous access to Amazon Data, eBay Data, Alibaba Data, and AliExpress Data by bypassing CAPTCHAs, Cloudflare, and other blocking mechanisms.

✔ Custom & R...

How does the structure of the peer review process, which can vary from journal to journal, influence the quality of papers published in that journal? In this paper, I study multiple systems of peer review using computational simulation. I find that, under any system I study, a majority of accepted papers will be evaluated by the average reader as not meeting the standards of the journal. Moreover, all systems allow random chance to play a strong role in the acceptance decision. Heterogeneous reviewer and reader standards for scientific quality drive both results. A peer review system with an active editor (who uses desk rejection before review and does not rely strictly on reviewer votes to make decisions) can mitigate some of these effects.

Replication materials for "A Review of Best Practice Recommendations for Text-Analysis in R (and a User Friendly App)". You can also find these materials on GitHub repo (https://github.com/wesslen/text-analysis-org-science) as well as the Shiny app in the GitHub repo (https://github.com/wesslen/topicApp).

TripAdvisor reviews and comparable data sources play an important role in many tasks in Natural Language Processing (NLP), providing a data basis for the identification and classification of subjective judgments, such as hotel or restaurant reviews, into positive or negative polarities. This study explores three important factors influencing variation in crowdsourced polarity judgments, focusing on TripAdvisor reviews in Spanish. Three hypotheses are tested: the role of Part Of Speech (POS), the impact of sentiment words such as “tasty”, and the influence of neutral words like “ok” on judgment variation. The study’s methodology employs one-word titles, demonstrating their efficacy in studying polarity variation of words. Statistical tests on mean equality are performed on word groups of our interest. The results of this study reveal that adjectives in one-word titles tend to result in lower judgment variation compared to other word types or POS. Sentiment words contribute to lower judgment variation as well, emphasizing the significance of sentiment words in research on polarity judgments, and neutral words are associated with higher judgment variation as expected. However, these effects cannot be always reproduced in longer titles, which suggests that longer titles do not represent the best data source for testing the ambiguity of single words due to the influence on word polarity by other words like negation in longer titles. This empirical investigation contributes valuable insights into the factors influencing polarity variation of words, providing a foundation for NLP practitioners that aim to capture and predict polarity judgments in Spanish and for researchers that aim to understand factors influencing judgment variation.

These datasets contain reviews from the Goodreads book review website, and a variety of attributes describing the items. Critically, these datasets have multiple levels of user interaction, raging from adding to a shelf, rating, and reading.

Metadata includes

• reviews

• add-to-shelf, read, review actions

• book attributes: title, isbn

• graph of similar books

Basic Statistics:

• Items: 1,561,465

• Users: 808,749

• Interactions: 225,394,930

Description:

This dataset contains information about data science books that were extracted from Amazon. The dataset includes the book title, author, price, ratings, and number of reviews. This information can be useful for anyone who is interested in data science and wants to explore popular books in the field.

The dataset can be used for various purposes such as analyzing trends in data science book sales, comparing authors and publishers, and identifying highly rated books with a large number of reviews. Additionally, the dataset can be used for training machine learning models to predict book popularity or pricing.

The dataset contains a total of 328 books, with each book having information on its title, author, price, ratings, and number of reviews. The data was scraped from Amazon using web scraping techniques.

Data Dictionary:

• Title: The title of the book
• Author: The author(s) of the book
• Price: The price of the book in US dollars
• Ratings: The average rating of the book on Amazon, on a scale of 1-5 stars
• Number of Reviews: The number of reviews the book has received on Amazon

I hope that this dataset will be useful for researchers, data scientists, and anyone interested in exploring data science books. Please let us know if you have any questions or feedback.