What Can Online Doctor Reviews Tell Us? A Deep Learning Assisted Study of Telehealth Service

dc.contributor.author Hao, Haijing
dc.contributor.author Zhang, Bin
dc.contributor.author Zhan, Yongcheng
dc.contributor.author Wu, Jiang
dc.date.accessioned 2022-12-27T19:02:42Z
dc.date.available 2022-12-27T19:02:42Z
dc.date.issued 2023-01-03
dc.description.abstract The present study develops a novel deep learning method which assists text mining of online doctor reviews to extract underlying sentiment scores. Those scores can be used to estimate a healthcare service quality model to investigate how the online doctor reviews impact the online doctor consultation demand. Based on the data from the largest online health platforms in China, our model results show that the underlying sentiment scores have statistically significant impacts on the demand of online doctor consultation. Theoretically, the present study constructs an innovative deep learning algorithm with a better performance than four widely used text mining methods, which can be applied to text mining of many online forums or social media texts. Empirically, our model results show what factors impact the health service quality and online doctor consultation demand, and following those factors, healthcare professionals can improve their service.
dc.format.extent 10
dc.identifier.doi 10.24251/HICSS.2023.265
dc.identifier.isbn 978-0-9981331-6-4
dc.identifier.uri https://hdl.handle.net/10125/102896
dc.language.iso eng
dc.relation.ispartof Proceedings of the 56th Hawaii International Conference on System Sciences
dc.rights Attribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.uri https://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subject Data Analytics, Data Mining, and Machine Learning for Social Media
dc.subject deep learning
dc.subject online doctor consultation service
dc.subject online doctor review
dc.subject sentiment score
dc.subject text mining
dc.title What Can Online Doctor Reviews Tell Us? A Deep Learning Assisted Study of Telehealth Service
dc.type.dcmi text
prism.startingpage 2130
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