## 4 Results
### Topic Modelling Results  
When the topic was set to 5 for Booking.com and Expedia, coherence scores of 0.71 and 0.67 were achieved, respectively, indicating a high level of semantic similarity and relevance between the topics. The coherence score is a value that measures the quality of a topic model and has a value between 0 and 1. One represents perfect coherence, and 0 represents no coherence. This shows that the five topics provide an effective balance in features that capture the required level of detail of the topics in the dataset without overly fragmenting the data. This optimal point suggests that our text collection is separated into five well-defined, distinct topics and validates the robustness of our LDA topic modelling approach [62]. In trials with a larger number of topics (10, 15, etc. ), the consistency score dropped to very low levels, and the topics that emerged were seen to be vague and difficult to interpret. Figures 5 and 6 show the estimated frequency of each term within the topic compared with the overall model frequency. The LDA topic visualisation tool pyLDAvis library was used for data visualisation. PyLDAvis is designed to help users interpret topics in a topic model as appropriate for a collection of text data. The blue colour shows the importance of that word in the general dataset, and the red colour shows the importance of that word in  
the selected topic. The red parts of a word indicate the more specific and meaningful the word is for that subject. Figure 5: LDA terms (Booking). (**a**) Topic 1; (**b**) Topic 2; (**c**) Topic 3; (**d**) Topic 4; (**e**) Topic 5. Figure 6: LDA terms (Expedia). (**a**) Topic 1; (**b**) Topic 2; (**c**) Topic 3; (**d**) Topic 4; (**e**) Topic 5. Five topics of the Booking.com application and the words belonging to the topic are shown in Figure 4. When the words in Topic 1, which is dominated by the words 'Application', 'thank', 'nice', booking', 'easy', 'use', 'useful','reservation', are examined, there are comments regarding satisfaction with the Booking.com application. There are also references to the ease of booking on the app. Additionally, there are expressions of appreciation for the application. Topic 2, which includes the words 'hotel', 'great', 'application','reservation', 'want', 'booking', 'find' and 'thanks', contains words that contain the functions of the application. Users have described the functions of finding the hotel they want and making a reservation as good. Functional problems predominate in the expressions in Topic 3, where the words 'booking', 'hotel', 'problems','reservations', 'application','made','reservation', 'payment', 'without' predominate. Problems with making reservations, problems originating from hotels outside the application, and payment problems are among the main problems mentioned in Topic 4, which includes the words'reservation', 'hotel', 'place','stay', 'find', 'accommodation', 'application', and 'one', contains words related to the services offered in the application. Finding a hotel, accommodation, and reservation are the most prominent services of the application. The features of the application are included in Topic 5, which includes the words 'fast','reliable', 'easy', 'useful', 'abroad','reservation', 'free', 'accommodation','site', 'options. Users have referred to the Booking.com application for its speed, reliability, ease, usability, international expansion, being free and offering options. Five topics of the Expedia application and graphs belonging to each topic are shown in Figure 5. Topic 1, which includes the words 'Expedia', 'app', 'get', 'hotel','service', 'use', 'great','refund', 'flight', 'customer', contains words that emphasise the services provided by the Expedia application to its customers, such as finding hotels, flights, and refund services. In Topic 2, where the words 'Expedia', 'hotel', 'app', 'use', 'room','money', 'booked','refund', 'booking', 'cancellation' is dominant, users commented on issues related to payments and refunds after cancellations in the Expedia application.