Mining service quality feedback from social media: A computational analytics method

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.