Automatic Classification of Online Doctor Reviews: Evaluation of Text Classifier Algorithms.
Background: An increasing number of doctor reviews are being generated by patients on the internet. These reviews address a diverse set of topics (features), including wait time, office staff, doctor's skills, and bedside manners. Most previous work on automatic analysis of Web-based customer review...
| Publicado en: | Journal of Medical Internet Research Vol. 20; no. 11; pp. 1 - 2 |
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| Autores principales: | , , , |
| Formato: | research Journal Article |
| Publicado: |
JMIR Publications Inc.
Nov2018
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=133419670&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 133419670 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 14394456 DNC jtl: Journal of Medical Internet Research issn: 14394456 maglogo: N pubinfo: dt: Nov2018 vid: 20 iid: 11 pid: 21567 pub: JMIR Publications Inc. place: Toronto, Ontario artinfo: ui: 133419670 133419670 NLM30425030 133419670 10.2196/11141 NLM30425030 133419670 ppf: 1 ppct: 1 formats: tig: atl: Automatic Classification of Online Doctor Reviews: Evaluation of Text Classifier Algorithms. aug: au: Rivas, Ryan Montazeri, Niloofar Le, Nhat XT Hristidis, Vagelis affil: Department of Computer Science and Engineering, University of California, Riverside, Riverside, CA, United States sug: subj: Clinical Indicators Standards Literature Physicians Attitude Human Internet Language Algorithms Validation Studies Comparative Studies Evaluation Research Multicenter Studies ab: Background: An increasing number of doctor reviews are being generated by patients on the internet. These reviews address a diverse set of topics (features), including wait time, office staff, doctor's skills, and bedside manners. Most previous work on automatic analysis of Web-based customer reviews assumes that (1) product features are described unambiguously by a small number of keywords, for example, battery for phones and (2) the opinion for each feature has a positive or negative sentiment. However, in the domain of doctor reviews, this setting is too restrictive: a feature such as visit duration for doctor reviews may be expressed in many ways and does not necessarily have a positive or negative sentiment.Objective: This study aimed to adapt existing and propose novel text classification methods on the domain of doctor reviews. These methods are evaluated on their accuracy to classify a diverse set of doctor review features.Methods: We first manually examined a large number of reviews to extract a set of features that are frequently mentioned in the reviews. Then we proposed a new algorithm that goes beyond bag-of-words or deep learning classification techniques by leveraging natural language processing (NLP) tools. Specifically, our algorithm automatically extracts dependency tree patterns and uses them to classify review sentences.Results: We evaluated several state-of-the-art text classification algorithms as well as our dependency tree-based classifier algorithm on a real-world doctor review dataset. We showed that methods using deep learning or NLP techniques tend to outperform traditional bag-of-words methods. In our experiments, the 2 best methods used NLP techniques; on average, our proposed classifier performed 2.19% better than an existing NLP-based method, but many of its predictions of specific opinions were incorrect.Conclusions: We conclude that it is feasible to classify doctor reviews. Automatically classifying these reviews would allow patients to easily search for doctors based on their personal preference criteria. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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