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--- |
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dataset_info: |
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features: |
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- name: comment |
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dtype: string |
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- name: quad |
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sequence: |
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sequence: string |
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- name: dataset |
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dtype: string |
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splits: |
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- name: train |
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num_bytes: 2111953 |
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num_examples: 3987 |
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- name: test |
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num_bytes: 266209 |
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num_examples: 500 |
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- name: validation |
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num_bytes: 88525 |
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num_examples: 170 |
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download_size: 1136999 |
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dataset_size: 2466687 |
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configs: |
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- config_name: default |
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data_files: |
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- split: train |
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path: data/train-* |
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- split: test |
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path: data/test-* |
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- split: validation |
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path: data/validation-* |
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--- |
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# OATS Dataset |
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## Description |
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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. |
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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. |
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Structure |
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The dataset is structured into two primary components: |
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Opinion Quadruples: Detailed annotations on the level of individual opinions, including the aspect, the sentiment target, and the corresponding sentiment. |
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Review-Level Tuples: Aggregate information at the review level, indicating the overall sentiment polarity for each aspect category mentioned. |
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## Domains |
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Amazon FineFood Reviews |
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Coursera Course Reviews |
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TripAdvisor Hotel Reviews |
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Each domain is annotated from scratch, ensuring high-quality data for nuanced sentiment analysis tasks. |
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Citation |
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If you use the OATS dataset in your research, please cite the original authors: |
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``` |
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@misc{chebolu2023oats, |
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title={OATS: Opinion Aspect Target Sentiment Quadruple Extraction Dataset for Aspect-Based Sentiment Analysis}, |
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author={Siva Uday Sampreeth Chebolu and Franck Dernoncourt and Nedim Lipka and Thamar Solorio}, |
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year={2023}, |
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eprint={2309.13297}, |
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archivePrefix={arXiv}, |
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primaryClass={cs.CL} |
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} |
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``` |
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## Usage |
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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: |
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``` |
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from datasets import load_dataset |
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dataset = load_dataset("jordiclive/OATS-ABSA") |
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``` |
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