# Transforming Customer Digital Footprints into Decision Enablers in Hospitality
###### Abstract  
The proliferation of online hotel review platforms has prompted decision-makers in the hospitality sector to acknowledge the significance of extracting valuable information from this vast source. While contemporary research has primarily focused on extracting sentiment and discussion topics from online reviews, the transformative potential of such insights remains largely untapped. In this paper, we propose an approach that leverages Natural Language Processing (NLP) techniques to convert unstructured textual reviews into a quantifiable and structured representation of emotions and hotel aspects. Building upon this derived representation, we conducted a segmentation analysis to gauge distinct emotion and concern-based profiles of customers, as well as profiles of hotels with similar customer emotions using a self-organizing unsupervised algorithm. We demonstrated the practicality of our approach using 22,450 online reviews collected from 44 hotels. The insights garnered from emotion analysis and review segmentation facilitate the development of targeted customer management strategies and informed decision-making.  
social media analytics; GuidedLDA; emotion modelling; unsupervised learning; decision support; online reviews +
Footnote †: journal: _Applied_  
0000-0001-9999-4000-00001-  
mentions of hotel aspects into a structured and quantifiable format that can be effectively applied for decision-making and analysis.  
Thus, the primary objective of this research is to generate a structured emotion-hotel aspect representation from unstructured online reviews to support decision-making. We adopt [PERSON]'s emotion model, which offers a more nuanced understanding with its eight emotions compared to broader sentiment categories [9]. Next, we showcase the use of derived emotion representations to conduct a segmentation analysis that illustrates different profiles of customer reviews based on the diverse emotions expressed. Such segmentation is particularly advantageous for the hospitality industry, given its diverse customer base expressing a range of emotions and concerns in reviews [5; 10].  
To achieve this, we employ Natural Language Processing (NLP) to extract emotions and hotel aspects and the Growing Self-Organizing Maps (GSOMs) algorithm [11], an unsupervised clustering technique, to generate profiles based on similar characteristics in data. Additionally, the GSOM algorithm serves as a visualization tool, aiding in the identification of these distinctive profiles.  
To demonstrate the practicality and applicability of using this approach in real-world scenarios, we have used 22,450 online reviews from 44 hotels from the Tripadvisor platform over three years. The rest of this paper is organized as follows. Section 2 discusses the role of online reviews in the tourism industry and outlines different approaches taken for emotion modelling and profiling. Section 3 provides the methodology to develop the emotion profile from online reviews, while Section 4 demonstrates the results of the analysis. The paper concludes with a discussion of the findings and potential implications, followed by future enhancements.