Repository files navigation

This repository contains the data of theNAACL 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 theDianping 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 pddata = 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