Automatic Classification of Online Doctor Reviews: Evaluation of Text Classifier Algorithms.

Abstract:

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.

journal_name

J Med Internet Res

authors

Rivas R,Montazeri N,Le NX,Hristidis V

doi

10.2196/11141

subject

Has Abstract

pub_date

2018-11-12 00:00:00

pages

e11141

issue

11

eissn

1439-4456

issn

1438-8871

pii

v20i11e11141

journal_volume

20

pub_type

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