In spite of the growing opportunities and demands for using social media to assist government decision-making, few studies have investigated social media sentiments toward public services due to the large volume and noisy nature of big data. Taking a design science approach, this paper suggests a systematic method to assign tweets into each of the SERVQUAL dimensions to identify sentiments and track perceived service quality of healthcare services for policy makers. The method consists of (1) identifying more reliable topic sets through repeated latent Dirichlet allocation (LDA) and clustering; and (2) classifying tweets using topics based on an existing theory for service quality. The method is applied to tweets on the quality of NHS of the UK to demonstrate its usability. We measured social perceptions of healthcare service quality and identified keywords for each SERVQUAL dimension. Moreover, a comparison between the social perceptions derived from the tweets and traditional survey result on the same service quality shows the similarity which confirms the usability of the proposed method. The method has a practical value as a complimentary tool for the more expensive national scale surveys as well as academic value as a novel method integrating text mining with theoretically sound quality framework, SERVQUAL.

尽管使用社交媒体协助政府决策的机会和需求不断增长，但由于大数据量大且噪声大，因此很少有研究调查社交媒体对公共服务的看法。本文采用设计科学的方法，提出了一种系统化的方法，将推文分配到每个SERVQUAL维度中，以识别情绪并跟踪决策者对医疗服务的感知服务质量。该方法包括：（1）通过重复潜在狄利克雷分配（LDA）和聚类识别更可靠的主题集；（2）使用基于现有服务质量理论的主题对推文进行分类。该方法适用于有关英国NHS质量的推文，以证明其可用性。我们评估了社会对医疗服务质量的看法，并确定了每个SERVQUAL维度的关键字。此外，从推文得出的社会认知与传统调查结果对相同服务质量的比较显示出相似性，证实了所提方法的可用性。该方法具有实用价值，可作为更昂贵的国家规模调查的补充工具，并且具有作为将文本挖掘与理论上合理的质量框架SERVQUAL集成在一起的新方法的学术价值。