Improving guest satisfaction by identifying hotel service micro-elements failures through Deep Learning of online reviews

Abstract

This study thoroughly examines often-overlooked micro-service elements within the broader spectrum of hotel services, aiming to improve hospitality services and ensure guest satisfaction. To achieve this, this research developed a methodological framework, integrating (a) the VADER text sentiment analysis framework, (b) a robust logistic regression procedure to pinpoint specific hotel service components culprit for guest frustration, and (c) the application of semantic network analysis to yield guest insights contextualised within the realm of underperforming hotel service micro-elements.
Research findings highlight fifty specific service micro-elements identified as triggers of negative sentiment and subsequent degrees of diminished guest satisfaction. Furthermore, this study zooms into the top ten underperforming service micro-elements by employing semantic network analysis to uncover the roots of typical guest frustrations with their hotel experiences. Though identified within hotel reviews, certain service malfunctions have relevance within the broader domain of destination management.
The outcomes of this study suggest a valuable resource for managers in detecting and rectifying inadequately performing hotel service micro-elements, which are pivotal for elevating guest satisfaction within their respective hotel properties. Additionally, the findings provide impetus for hotel and destination managers to implement tailored strategies to increase guest satisfaction across hotels and destinations.