Exploring consumer's perception of service quality through online reviews: Text mining approach

Online reviews are an important source of consumer opinions about the service experiences across industries. Growing number of research investigate the potential of online reviews to explain consumer behaviour and to predict business performance, while only few engage into the examination of the textual content of the reviews. Large volume and unstructured format of the textual data make it unamenable to analysis with traditional methods like survey. To fill the emerging gap, this study will rely on automated text mining methodology to explore how consumers reflect on the perceived service quality in the online reviews. Specifically, sentiment analysis, text classification and regression modelling will be applied to investigate the dimensions of perceived service quality together with their contribution to the overall rating of service experience (e.g. star rating). The results are expected to extend the understanding of perceived service quality; to illustrate the value of the automated text mining methods for maximising the usefulness of online reviews and helping businesses reach their strategic goals.