license: mit
language:
- en
pretty_name: Book Review Text Data
Dataset Card for Book Text Data
This dataset provides text-based reviews for fiction and nonfiction books.
Dataset Details
Dataset Description
For a selection of books on my bookshelf, I collected some text data. I selected 15 fiction and 15 nonfiction books. I then wrote three reviews for each book to create the first 90 examples, and then I wrote 5 hypothetical fiction book reviews and 5 hypothetical nonfiction book reviews. These reviews were collected for the 30 books + 10 hypotheticals that make up my original split, and then they were augmented to create 1600 additional examples.
- Curated by: Jennifer Evans
- Language(s) (NLP): English
- License: MIT License
Uses
Direct Use
Intended use: evaluating if a book is fiction or nonfiction based on the review.
Out-of-Scope Use
This dataset could be used for other book-related evaluations, like how people describe books and what their book preferences are. It could also be used to evaluate subgenres of fiction and nonfiction, such as sci-fi versus fantasy.
Dataset Structure
dataset_info:
features:
name: Review
dtype: string
name: Fiction?
dtype: int64
- splits:
name: original
num_bytes: 15506
num_examples: 100
name: augmented
num_bytes: 247944
num_examples: 1600
download_size: 52247
dataset_size: 263450
- configs:
config_name: default
data_files:
split: original
path: data/original-*
split: augmented
path: data/augmented-*
Dataset Creation
Curation Rationale
As an avid reader, I was drawn to reviewing some of the recent books I've read. I was especially interested in evaluating how my reviews may differ from the professional development style books I read (nonfiction) from the sci-fi and fantasy books I read (fiction).
Source Data
Books were selected from my bookshelf. I aimed to pull a variety of books, spanning different genres and series.
Data Collection and Processing
I selected 30 representative books from my bookshelf and then wrote 100 original reviews, including 3 reviews for each book. This data was then augmented with NLTK methods like synonym swapping to create the augmented dataset.
Who are the source data producers?
Jennifer Evans wrote the reviews for each book. She reads a variety of fiction and nonfiction books, mainly on professional development, wellness, sci-fi, and fantasy.