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Understanding patient reviews with minimum supervision

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Gui, Lin and He, Yulan (2021) Understanding patient reviews with minimum supervision. Artificial Intelligence in Medicine, 120 . 102160. doi:10.1016/j.artmed.2021.102160 ISSN 0933-3657.

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Official URL: https://doi.org/10.1016/j.artmed.2021.102160

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Abstract

Understanding patient opinions expressed towards healthcare services in online platforms could allow healthcare professionals to respond to address patients’ concerns in a timely manner. Extracting patient opinion towards various aspects of health services is closely related to aspect-based sentiment analysis (ABSA) in which we need to identify both opinion targets and target-specific opinion expressions. The lack of aspect-level annotations however makes it difficult to build such an ABSA system. This paper proposes a joint learning framework for simultaneous unsupervised aspect extraction at the sentence level and supervised sentiment classification at the document level. It achieves 98.2% sentiment classification accuracy when tested on the reviews about healthcare services collected from Yelp, outperforming several strong baselines. Moreover, our model can extract coherent aspects and can automatically estimate the distribution of aspects on different polarities without requiring aspect-level annotations for model learning.

Item Type: Journal Article
Subjects: Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software
R Medicine > R Medicine (General)
Divisions: Faculty of Science, Engineering and Medicine > Science > Computer Science
Library of Congress Subject Headings (LCSH): Artificial intelligence -- Medical applications, Sentiment analysis , Data mining -- Health aspects, Outcome assessment (Medical care) -- Simulation methods, Patient satisfaction , Communication in medicine
Journal or Publication Title: Artificial Intelligence in Medicine
Publisher: Elsevier BV
ISSN: 0933-3657
Official Date: October 2021
Dates:
DateEvent
October 2021Published
1 September 2021Available
16 August 2021Accepted
Volume: 120
Article Number: 102160
DOI: 10.1016/j.artmed.2021.102160
Status: Peer Reviewed
Publication Status: Published
Access rights to Published version: Restricted or Subscription Access
Date of first compliant deposit: 15 September 2021
Date of first compliant Open Access: 1 September 2022
RIOXX Funder/Project Grant:
Project/Grant IDRIOXX Funder NameFunder ID
794196Horizon 2020 Framework Programmehttp://dx.doi.org/10.13039/100010661
EP/V048597/1[EPSRC] Engineering and Physical Sciences Research Councilhttp://dx.doi.org/10.13039/501100000266
EP/T017112/1[EPSRC] Engineering and Physical Sciences Research Councilhttp://dx.doi.org/10.13039/501100000266
EP/V020579/1UK Research and Innovationhttp://dx.doi.org/10.13039/100014013
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