基于文本挖掘的在线医疗平台综合评价研究

Research on Comprehensive Evaluation of Online Medical Platform Based on Text Min-ing

随着防疫政策的放开,传统医疗资源有限,在线医疗一定程度上成为人们“病有所医”的保障。然而,我国目前主流的在线医疗平台存在产品创新不足、服务质量不高、各类产品同质化严重等的问题,导致可能无法给用户提供有建设性有效的医疗建议。随着互联网技术的广泛应用以及人们对便利的医疗服务的需要,完善在线医疗平台的服务是十分有必要的。影响用户对在线医疗平台的满意度的因素是什么?在线医疗平台如何有针对性地完善平台功能?用户如何选择合适的在线医疗平台?这些都是当下需要解决的问题。本文基于从网络上爬取的主流的在线医疗平台的评论文本,运用文本分析技术、聚类算法、层次分析法、情感分析等技术搭建满意度模型,对影响用户对平台满意度的因素进行研究,为平台提高自身功能和服务,以及为用户选择在线医疗平台提供建议和参考。

With the release of epidemic prevention policies, traditional medical resources are limited, and online medical treatment has become a guarantee for people to have access to medical services to some extent. However, China’s mainstream online medical platforms have problems such as insuffi-cient product innovation, poor service quality and serious homogenization of various products, which may lead to the inability to provide users with constructive and effective medical advice. With the wide application of Internet technology and people’s need for convenient medical services, it is very necessary to improve the service of online medical platform. What factors affect users’ sat-isfaction with online medical platforms? How to improve the function of online medical platform? How do users choose the right online medical platform? These are the issues that need to be ad-dressed right now. Based on the comments of mainstream online medical platforms crawled from the Internet, this paper uses text analysis technology, clustering algorithm, analytic hierarchy pro-cess, emotion analysis and other technologies to build a satisfaction model, and studies the factors that affect users’ satisfaction with the platform, so as to provide suggestions and references for the platform to improve its own functions and services and for users to choose online medical platforms.