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: bujh1994@gmail.com
Lei Ren: renlei04@meituan.com
Jingang Wang: wangjingang02@meituan.com