| | --- |
| | dataset_info: |
| | features: |
| | - name: comment |
| | dtype: string |
| | - name: quad |
| | sequence: |
| | sequence: string |
| | - name: dataset |
| | dtype: string |
| | splits: |
| | - name: train |
| | num_bytes: 2111953 |
| | num_examples: 3987 |
| | - name: test |
| | num_bytes: 266209 |
| | num_examples: 500 |
| | - name: validation |
| | num_bytes: 88525 |
| | num_examples: 170 |
| | download_size: 1136999 |
| | dataset_size: 2466687 |
| | configs: |
| | - config_name: default |
| | data_files: |
| | - split: train |
| | path: data/train-* |
| | - split: test |
| | path: data/test-* |
| | - split: validation |
| | 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") |
| | ``` |
| |
|
| |
|
