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---
dataset_info:
features:
- name: comment
dtype: string
- name: quad
sequence:
sequence: string
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num_examples: 3987
- name: test
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num_examples: 500
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num_examples: 170
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configs:
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data_files:
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path: data/train-*
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path: data/test-*
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path: data/validation-*
---
# OATS Dataset
## Description
The OATS (Opinion Aspect Target Sentiment) dataset is a comprehensive collection designed for the Aspect Sentiment Quad Prediction (ASQP) or Aspect-Category-Opinion-Sentiment (ACOS) task. This dataset aims to facilitate research in aspect-based sentiment analysis by providing detailed opinion quadruples extracted from review texts. Additionally, for each review, we offer tuples summarizing the dominant sentiment polarity toward each aspect category discussed.
The dataset covers three distinct domains: Amazon FineFood reviews, Coursera course reviews, and TripAdvisor Hotel reviews, offering a broad spectrum for analysis across different types of services and products.
Structure
The dataset is structured into two primary components:
Opinion Quadruples: Detailed annotations on the level of individual opinions, including the aspect, the sentiment target, and the corresponding sentiment.
Review-Level Tuples: Aggregate information at the review level, indicating the overall sentiment polarity for each aspect category mentioned.
## Domains
Amazon FineFood Reviews
Coursera Course Reviews
TripAdvisor Hotel Reviews
Each domain is annotated from scratch, ensuring high-quality data for nuanced sentiment analysis tasks.
Citation
If you use the OATS dataset in your research, please cite the original authors:
```
@misc{chebolu2023oats,
title={OATS: Opinion Aspect Target Sentiment Quadruple Extraction Dataset for Aspect-Based Sentiment Analysis},
author={Siva Uday Sampreeth Chebolu and Franck Dernoncourt and Nedim Lipka and Thamar Solorio},
year={2023},
eprint={2309.13297},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
## Usage
This dataset has been curated to facilitate easy access and integration into existing NLP pipelines. To use this dataset, you can load it using the datasets library by Hugging Face:
```
from datasets import load_dataset
dataset = load_dataset("jordiclive/OATS-ABSA")
```
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