license:mit region:us
---
license: mit
---
# The Impact of Book-to-Movie Adaptations

## Executive Summary

This dataset explores the relationship between literary works and their film/television adaptations, focusing on how adaptations influence the reception and appreciation of original works. The dataset contains information about books that have been adapted into films or TV shows, including their ratings, popularity metrics, and audience engagement data from Goodreads.

Data Sourcing Code Repository: https://github.com/SSSSShi/Movie-Adaptations-Dataset


### Motivation
The film and television industry's increasing focus on IP adaptations has created a need to understand how these adaptations affect the original literary works. This dataset aims to provide insights into:
- The relationship between book popularity and adaptation success
- Characteristics of successfully adapted literary works

### Potential Applications
- Analysis of adaptation success factors
- Prediction of potential successful adaptation candidates
- Understanding audience reception patterns
- Strategic decision-making for publishers and producers
- IP valuation and marketing strategy development

## Data Description

The dataset contains the following main fields:
- `Name`: Title of the book
- `Author`: Author of the book
- `Avg Rating`: Average rating on Goodreads (scale 0-5)
- `Rating Count`: Number of ratings received
- `Score`: Goodreads list score
- `Vote Count`: Number of votes received on the adaptation list

### Data Statistics
```
Total number of books: 434
Most books rate between: 3.57 - 4.39 stars
Average rating: 3.99
```
## Previous Datasets Review

Existing datasets in this domain include:

1. **IMDb Datasets**
   - Contains basic movie information
   - Lacks direct connection to source material
   - No book-specific metrics

2. **Goodreads Datasets**
   - Focus only on book metrics
   - No adaptation information
   - Limited to reading metrics

3. **MovieLens**
   - Movie ratings and metadata
   - No book adaptation information
   - Limited to viewer preferences

Our dataset is novel in that it:
- Combines both book and adaptation metrics
- Enables direct analysis of adaptation impact


## Power Analysis

The dataset includes 434 books with their adaptations, spanning various genres and time periods. This sample size provides sufficient statistical power for:
- Correlation analysis between ratings and popularity metrics
- Comparative analysis of pre/post adaptation reception

## Exploratory Data Analysis

### Key Findings:

1. **Rating Distribution**
   - Average ratings show a negatively skewed distribution
   - Most books rate between 3,57 and 4.39 stars

2. **Popularity Metrics**
   - Strong correlation between rating count and score (r = [correlation coefficient])
   - Top rated books tend to have higher vote counts

3. **Most Popular Adaptations**
   - Top Books by Rating:
     1. Harry Potter and the Deathly Hallows (Harry Potter, #7) by J.K. Rowling (Rating: 4.62)
     2. Harry Potter and the Prisoner of Azkaban (Harry Potter, #3) by J.K. Rowling (Rating: 4.58)
     3. Harry Potter and the Half-Blood Prince (Harry Potter, #6) by J.K. Rowling (Rating: 4.58)
   - Top Books by Popularity (Rating Count):
     1. Harry Potter and the Sorcerer's Stone (Harry Potter, #1) by J.K. Rowling (10,508,696 ratings)
     2. The Hunger Games (The Hunger Games, #1) by Suzanne Collins (9,043,765 ratings)
     3. Twilight by Stephenie Meyer (6,825,359 ratings)

    ![alt text](eda_analysis.png)

## Code Repository

The data collection and analysis code is available at [GitHub Repository Link]. The repository includes:
- Web scraping scripts for Goodreads data collection
- Data cleaning and preprocessing scripts
- Exploratory data analysis and visualization

## Ethics Statement

This dataset has been compiled with careful consideration of ethical implications:

1. **Data Collection**: All data was collected through public APIs and web scraping in compliance with Goodreads' terms of service.

2. **Privacy**: Only publicly available information has been included in the dataset.

3. **Bias Considerations**:
   - Language bias: Dataset primarily contains English-language books
   - Platform bias: Data is limited to Goodreads users' demographics

4. **Usage Guidelines**: This dataset should be used with awareness of these limitations and biases.

## License

This dataset is released under the MIT License. You are free to:

Copyright (c) 2024 Reina Shi

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