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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
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 | |||||||||||||||
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| Subjects: | Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software R Medicine > R Medicine (General) |
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| 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: |
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| 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: |
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