%0 Journal Article
%@ 1438-8871
%I JMIR Publications
%V 20
%N 11
%P e11141
%T Automatic Classification of Online Doctor Reviews: Evaluation of Text Classifier Algorithms
%A Rivas,Ryan
%A Montazeri,Niloofar
%A Le,Nhat XT
%A Hristidis,Vagelis
%+ Department of Computer Science and Engineering, University of California, Riverside, 363 Winston Chung Hall, 900 University Avenue, Riverside, CA, 92521, United States, 1 951 827 2838, rriva002@ucr.edu
%K patient satisfaction
%K patient reported outcome measures
%K quality indicators, health care
%K supervised machine learning
%D 2018
%7 12.11.2018
%9 Original Paper
%J J Med Internet Res
%G English
%X 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. 
%M 30425030
%R 10.2196/11141
%U http://www.jmir.org/2018/11/e11141/
%U https://doi.org/10.2196/11141
%U http://www.ncbi.nlm.nih.gov/pubmed/30425030