This repository contains the data of the NAACL 2021 paper: ASAP: A Chinese Review Dataset Towards Aspect Category Sentiment Analysis and Rating

ASAP is a large-scale Chinese restaurant review dataset for Aspect category Sentiment Analysis (ACSA) and review rating Prediction (RP).

ASAP includes 46, 730 genuine user reviews from the Dianping App, a leading Online-to-Offline (O2O) e-commerce platform. Besides a 5-star scale rating, each review is manually annotated according to its sentiment polarities towards 18 pre-defined aspect categories, including food, service, enrionment and so on. We split the dataset into a training set (36,850), a validation set (4,940) and a test set (4,940) randomly.

import pandas as pd

data = pd.read_csv(file_path, header=0)

The sentiment polarity over the aspect category is labeled as 1(Positive), 0(Neutral), −1(Negative), −2(Not-Mentioned)

The star rating ranges from 1 to 5.

Please cite the following paper if you found it useful in your work.

@inproceedings{bu-etal-2021-asap,
    title = "{ASAP}: A {C}hinese Review Dataset Towards Aspect Category Sentiment Analysis and Rating Prediction",
    author = "Bu, Jiahao  and
      Ren, Lei  and
      Zheng, Shuang  and
      Yang, Yang  and
      Wang, Jingang  and
      Zhang, Fuzheng  and
      Wu, Wei",
    booktitle = "Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies",
    month = jun,
    year = "2021",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2021.naacl-main.167",
    pages = "2069--2079"
}

Jiahao Bu: [email protected]

Lei Ren: [email protected]

Jingang Wang: [email protected]