Goodreads-Books / README.md
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metadata
license: other
license_name: bright-data-master-service-agreement
license_link: https://brightdata.com/license
task_categories:
  - text-classification
  - summarization
  - text-generation
  - text2text-generation
language:
  - en
tags:
  - books
  - literature
  - reading
  - reviews
  - authors
  - ratings
  - genre analysis
  - book summaries
  - AI
  - NLP
  - Natural Language Processing
  - Machine Learning
  - ML
  - entertainment
size_categories:
  - 1M<n<10M

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Dataset Card for "BrightData/Goodreads-Books"

If you are using this dataset, we would love your feedback: Link to form.

Dataset Summary

Explore a collection of millions of books with the Goodreads dataset, comprising over 6.3M structured records and 14 data fields.

Continuously updated and verified for accuracy, this dataset provides invaluable insights for researchers, analysts and book enthusiasts alike.

Each entry includes all major data points such as including URLs, book IDs, titles, authors, star ratings, number of ratings, reviews, summaries, genres, publication dates, author details, community reviews and prices.

For a complete list of data points, please refer to the full "Data Dictionary" provided below.

To explore additional free and premium datasets, visit our website brightdata.com.

Data Dictionary

Column name Description Data type
url URL of the book Text
id Book id on the website Text
name Name of the book Text
author Book author Array
star_rating Star rating of the book 1-5 Number
num_ratings Number of people who left a rating Number
num_reviews Number of reviews on the book Text
summary Book summary Text
genres Book genre Array
first_published First published date Text
about_author About the author Object
> name Author name Text
> num_books Number of books by the author Number
> num_followers About the author Text
community_reviews Community reviews breakdown Object
> 5_stars 5 stars Object
>> reviews_num Number of reviews Number
>> reviews_percentage Percentage of reviews Number
> 4_stars 4 stars Object
>> reviews_num Number of reviews Number
>> reviews_percentage Percentage of reviews Number
> 3_stars 3 stars Object
>> reviews_num Number of reviews Number
>> reviews_percentage Percentage of reviews Number
> 2_stars 2 stars Object
>> reviews_num Number of reviews Number
>> reviews_percentage Percentage of reviews Number
> 1_stars 1 stars Object
>> reviews_num Number of reviews Number
>> reviews_percentage Percentage of reviews Number
kindle_price The price of the book on kindle Price

Dataset Creation

Data Collection and Processing

The data collection process involved extracting information directly from Goodreads, ensuring comprehensive coverage of the required attributes. Once collected, the data underwent several stages of processing:

  • Parsing: Extracted raw data was parsed to convert it into a structured format.
  • Cleaning: The cleaning process involved removing any irrelevant or erroneous entries to enhance data quality.

Validation

To ensure data integrity, a validation process was implemented. Each entry is checked across various attributes, including:

  • Uniqueness: Each record was checked to ensure it was unique, eliminating any duplicates.
  • Completeness: The dataset was examined to confirm that all necessary fields were populated or filled, with missing data addressed appropriately.
  • Consistency: Cross-validation checks were conducted to ensure consistency across various attributes, including comparison with historical records.
  • Data Types Verification: Ensured that all data types were correctly assigned and consistent with expected formats.
  • Fill Rates and Duplicate Checks: Conducted comprehensive checks to verify fill rates, ensuring no significant gaps in data, and rigorously screened for duplicates.

This ensures that the dataset meets the high standards of quality necessary for analysis, research and modeling.

Example JSON

[
  {
    "timestamp": "2024-05-09",
    "url": "https://www.imdb.com/title/tt1533087/",
    "title": "Soda Springs",
    "popularity": null,
    "genres": [
      "Drama"
    }
  ]