基于文本挖掘方法的移动医疗服务质量分析:Alodokter和Halodoc

N. Paramita, Siska Noviarisanti
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引用次数: 4

摘要

卫生服务的数字化转型导致印度尼西亚的数字卫生服务提供者数量不断增加。用户在在线平台上分享他们的经验并相互评论。本研究旨在通过大数据方法了解印尼移动医疗服务用户对移动医疗服务质量的看法。使用文本挖掘的研究源于对应用程序Alodokter和Halodoc的评论结果。用户生成内容是在2020年4月至12月期间从谷歌Play商店平台收集的。根据情绪分析,Alodokter表现良好,有73%的正面和27%的负面,而Halodoc也以86%的正面和14%的负面占主导地位。根据卫生服务质量的三个维度对用户评论进行多类分类。通过文本网络分析,可以识别出用户评论中经常出现的词网络。主要评价Alodokter和Halodoc的维度是感知结果质量。本研究的结果可以帮助或作为指导,作为评价的参考,以提高印度尼西亚的移动医疗服务质量。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Service Quality Analysis of Mhealth Services Using Text Mining Method : Alodokter and Halodoc
The digital transformation of health services causes an increasing number of digital health service providers in Indonesia. The user shares their experiences and reviews each other on the online platform. This study aims to understand user perceptions of m-health services in Indonesia based on m-health service quality with a big data approach. Research using text mining is derived from the results of the reviews of the application Alodokter and Halodoc. User-generated content was gathered from the platform Google Play Store in the period April to December 2020. Based on the sentiment analysis, Alodokter performs well with 73% positive and 27% negative, while Halodoc also dominated with 86% positive and 14% negative. User reviews are categorized based on three dimensions of health service quality with a multiclass classification. It is possible to identify the word networks that often appear in user reviews through text network analysis. The dimension that reviews chiefly on Alodokter and Halodoc is perceived outcome quality. The result of this study could help or use as guidance to be a reference for evaluations to improve Indonesia's quality of m-health services.
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