# Transforming Customer Digital Footprints into Decision Enablers in Hospitality
## 3 Materials and Methods
### Emotion Modelling
#### 3.2.2 Generating 'Emotion-Aspect Profile'  
The integration of emotion extraction (Section 3.2.1) and aspect extraction (Section 3.2.1) methodologies allowed for the development of an insightful 'Emotion-Aspect profile' for each online review, as depicted in Figure 4. This profile presents a structured and quantitative representation, encompassing hotel aspects and profound emotions, making it easily interpretable. In the context of emotion modelling and psychology research, the concept of emotion dynamics outlines emotion intensity as a feature that is essential to modelling emotion behaviours [44]. The emotion intensity profile, a key element of emotion dynamics features, portrays the variation in emotions at a specific moment. The derived emotion intensity profile for a given review, customer or hotel demonstrates the aggregation of emotions in a given time point. Since these techniques can be efficiently scaled to handle large volumes of data, the proposed platform is convenient for analysing content within social media and can be adjusted for diverse domains by fine-tuning the vocabulary. As this representation can be constructed at the review level (the most granular form), the emotion-aspect profiles  
Figure 4: The combined approach in creating the emotion-aspect profile for online reviews. Emotion expressions are noted in Yellow while hotel aspects are denoted in different colours. Figure 3: The high-level process of the hybrid aspect extraction approach. can be aggregated based on time, individual customers or hotels to form a comprehensive representation of their behaviours. This quantified representation, with its variables of emotions and hotel aspects, can be transformed into a vector representation, serving as input for many analytics and machine learning tasks. In our study, we employed this representation for emotion profiling. An Unsupervised Clustering Approach for Benchmarking Hotels Using Customer Experiences on Social Media  
Based on the emotion-aspect representations derived from the above approach, further evaluation was carried out to segment reviews, customers and hotels based on digital traces on social media. Given the unlabelled nature of the data, profiling was carried out using an unsupervised clustering algorithm. By aggregating emotion-aspect profiles at different levels, the following profiling analyses were carried out. * Profiling online reviews;
* Profiling customers;
* Profiling hotels. The unsupervised clustering of hotel emotion-aspect profiles was formed using an improved variant of the GSOM [11]. The GSOM introduces a map topography that self-structures by adapting its size and shape. The GSOM network structure starts with a minimal number of nodes (typically four nodes) and grows on boundaries based on heuristics and input representation. The GSOM consists of two phases; first, the growing phase where the neural network grows new nodes and adjusts node weights to sufficiently represent the input space, and second, the smoothing phase in which the node weights are fine-tuned. The growth of the GSOM is determined by the number of dimensions in the input space and the spread factor (SF), where the SF can be utilized to control the spread of the neural network structure independent of the dimensionality of the dataset [11]. In contrast to other clustering algorithms, the GSOM preserves the structural representation of the underlying data. Most of other clustering algorithms require a defined map representation or pre-defined number of clusters. However, the GSOM is able to grow the nodes based on features of the input data space. It also has the ability to discard the outdated information and restrict overfitting knowledge in its knowledge acquisition, therefore preserving the stability [45]. Since the GSOM provides a spatial representation of reviews, it provides an indication of groupings with similar characteristics based on the emotions and hotel aspects.