Knowledge Discovery of Service Satisfaction Based on Text Analysis of Critical Incident Dialogues and Clustering Methods

Abstract

Text mining of consumer's dialogues regarding their service experiences provides a direct and unbiased feedback to service providers. This research proposes an analysis process to analyze unstructured input from consumer dialogues. The goal is to apply the critical incident and text mining methods to discover factors that contribute to customer satisfaction and dissatisfaction. The critical incident method is used to construct an open-ended questionnaire to collect customer's positive and negative opinions toward the service provided. Valid and reliable text mining techniques are used to cluster significant text to help analyze incidents that customers care about. A case study of consumers riding the Kaohsiung Mass Rapid Transit System (KMRT) was cased to evaluate the proposed analysis process. Based on dialogues collected from the open-ended questionnaires, the analysis process extracts key phrases related to consumer's best and worst service experiences, creates significant dialogue clusters, and derives meaningful trends, baselines, and interpretations of consumer satisfaction and dissatisfaction. The results of this case study can be used as a basis for building more complete analytical methods to understand consumer experiences and provide strategic feedback for service providers.