---
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
- Goodreads
- book reviews
- LLM
size_categories:
- 1M<n<10M
---
![Bright Data Logo](https://brightdata.com/wp-content/uploads/2024/06/Bright-Data-logo-removebg-preview.png)

# Dataset Card for "BrightData/Goodreads-Books"

If you are using this dataset, we would love your feedback: [Link to form](https://docs.google.com/forms/d/e/1FAIpQLSdhxzlCEA8SGjjWZp-U6539oYmzdxlQ_Qb0pTPV6PqvmyrygA/viewform?usp=sf_link).

## Dataset Summary

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

Each entry includes all major data points such as including URLs, book IDs, titles, authors, ratings, number of ratings, reviews, summaries, genres, publication dates, author details 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](https://www.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

<div style="max-height: 300px; overflow-y: auto; border: 1px solid #ccc; padding: 10px;">

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