This repository contains the datasets used for the ICWSM'22 paper Effects of Multi-Aspect Online Reviews with Unobserved Confounders: Estimation and Implication and the WSDM'22 paper [Estimating Causal Effects of Multi-Aspect Online Reviews with Multi-Modal Proxies].
Each dataset (i.e., Toronto and LV datasets) has two files: the .txt file contains the popular times of each restaurant; the corresponding information of this restaurant such as entire reviews and business ID can be downloaded at LV_restaurants and Toronto_restaurants. You can use the business ID to access other information (e.g., location) of a restaurant in the Yelp dataset. We also include the datasets used for training the multi-aspect sentiment analysis classifier.
Please cite our paper if you use the datasets:
@article{cheng2021effects,
title={Effects of Multi-Aspect Online Reviews with Unobserved Confounders: Estimation and Implication},
author={Cheng, Lu and Guo, Ruocheng and Candan, Kasim Selcuk and Liu, Huan},
journal={arXiv preprint arXiv:2110.01746},
year={2021}
}
[1] Lu Cheng, Ruocheng Guo, Kasim Selcuk Candan and Huan Liu. Effects of Multi-Aspect Online Reviews with Unobserved Confounders: Estimation and Implication. International AAAI Conference on Web and Social Media (ICWSM), 2022.