# Transforming Customer Digital Footprints into Decision Enablers in Hospitality
## 4 Results
### Emotion-Aspect Representation and Hotel Segmentation  
This approach allows for creating an individualized visualization for each hotel based on the emotions and hotel aspects providing customized insights. The following Figure 5 shows the emotion-aspect profile of two hotels based on their customer comments recorded on social media. A comparison of emotion-aspect profiles of multiple hotels enables the identification of the unique characteristics of each hotel. In Hotel A, there is increased negativity expressed towards 'Room' given the amplified intensities in 'Sad', 'Disgust' and 'Anger'. As all reviews are structured to represent emotion variations, this approach allows managers to drill down to each emotion level to understand the causes for such negativity. In contrast, Hotel A shows increased positive emotions for 'Staff' as explained by higher intensities in 'Trust' and 'Joy'. Compared to Hotel A, Hotel B showcases a varied emotion profile with distinct characteristics. More customers have expressed an increased level of 'Anger' and 'Sad' for aspects 'Staff' and 'Food&Beverage'. Notably, the aspects 'Public Areas' and 'Bathroom' have recorded higher intensity of 'Surprise'. Such analysis highlights the best-performing and poor-performing hotel aspects, thereby providing insights into the aspects that need attention. In this experiment, we utilised the emotion intensity profile to model the emotion variation corresponding to hotel aspects over time. At each time point, it provides a holistic view of the customer emotions directed towards the hotel, as shown in Figure 6. This illustrates the intensities of emotions for each hotel aspect over three years. As observed, the constellation of emotions has changed over time for each hotel aspect. Similar intensity profiles can be generated for granular levels such as days, weeks or months by aggregating the corresponding reviews in that period. Once integrated with a real-time social media data stream, this enables visualisation of the emotion fluctuations in near real-time, thus enabling a closer inspection of emotions presented on social media. Based on the derived emotion-aspect profiles, further analysis was conducted using the GSOM algorithm to create a segmentation of all 44 hotels based on the derived deep emotion intensity values. The 44 hotels' emotion variations per each aspect were fed into  
Figure 5: Comparison of emotion-aspect profiles for two hotels. Figure 6: Demonstration of customer emotion fluctuations over time for a hotel. the GSOM to create a benchmarking visualisation. It was noted that there were four distinct clusters (profiles) based on their emotion variations. As the hotels that have been grouped showcase similar characteristics, the hotels can identify other similar hotels that perform similarly. Figure 7 illustrates the identified profiles and their distinct characteristics based on customer emotions. The most prominent aspect of hotels in Cluster 1 is 'Room', given its high intensity in all emotions compared to other aspects. It is also noted that higher negativity is shown for this aspect. Cluster 2 also shows increased negativity for 'Room' as denoted via high intensities in 'Anger', 'Sad', 'Fear' and 'Disgust'. However, in this group, there is increased positivity shown for the aspect 'Staff'. Cluster 3 shows a different distribution of emotions with higher intensity in 'Sad' for 'Exterior', which represents the design, appearance and ambience of the hotel. There is also an increased level of 'Anticipation' directed towards the aspect 'Room'. The hotels in Cluster 4 have received an increased level of 'Anger', 'Disgust' and 'Sad' for 'Food&Beverage', representing customers' dissatisfaction with the refreshments. Other aspects do not exhibit a clear difference. In a holistic view, the model provides an overview of different groupings of hotels in the industry.