## 4 Results
### Word Cloud Result  
Visualisation is the process of representing text mining results visually. This can be achieved using word clouds, graphs, maps, or other visual aids. Visualisation can help us more easily understand patterns and relationships in text data. Word Clouds provide a cloud-like display of words sized based on the frequency of words in texts [60]. In this study, the Seaborn library used in the Python programming language was used for data visualisation. This library provides predefined styles and colour palettes to facilitate the data visualisation process [61]. Figures 2-4 below show the positive, negative, and neutral word clouds for the Booking.com and Expedia applications, respectively. The word clouds were analysed by comparing the prominent words for each brand. \begin{table}
\begin{tabular}{l l l} \hline \hline
**Positive** & **Negative** & **Neutral** \\ \hline (‘Expedia’, 305) & (‘app’, 232) & (‘app’, 31) \\ (‘app’, 239) & (‘booking’, 200) & (‘Expedia’, 16) \\ (‘hotel’, 190) & (‘Expedia’, 192) & (‘booking’, 32) \\ (‘booking’, 172) & (‘hotel’, 146) & (‘need’, 10) \\ (‘use’, 139) & (‘use’, 128) & (‘find’, 10) \\ (‘great’, 147) & (‘service’, 77) & (‘time’, 9) \\ (‘room’, 76) & (‘time’, 75) & (‘issues’, 8) \\ (‘easy’, 66) & (‘room’, 73) & (‘charged’, 8) \\ (‘customer’, 64) & (‘customer’, 71) & (‘stay’, 8) \\ (‘flight’, 64) & (‘flight’, 66) & (‘problem’, 7) \\ (‘service’, 56) & (‘trying’, 52) & (‘try’, 7) \\ (‘travel’, 55) & (‘money’, 51) & (‘email’, 7) \\ (‘refund’, 54) & (‘help’, 44) & (‘people’, 7) \\ (‘trip’, 52) & (‘tried’, 44) & (‘review’, 7) \\ (‘price’, 52) & (‘refund’, 43) & (‘helpful’, 7) \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Expedia word frequency list. \begin{table}
\begin{tabular}{l l l} \hline \hline
**Positive** & **Negative** & **Neutral** \\ \hline (‘application’, 194) & (‘hotel’, 38) & (‘application’, 22) \\ (‘hotel’, 168) & (‘reservation’, 26) & (‘hotel’, 20) \\ (‘made’, 108) & Booking 25 & (‘find’, 14) \\ (‘booking’, 93) & (‘application’, 23) & (‘problems’, 13) \\ (‘easy’, 90) & (‘made’, 20) & (‘reservation’, 12) \\ (‘reservation’, 86) & (‘said’, 15) & (‘make’, 11) \\ (‘nice’, 81) & (‘money’, 14) & (‘Thank’, 8) \\ (‘Thank’, 74) & (‘find’, 13) & (‘use’, 8) \\ (‘great’, 63) & (‘bad’, 11) & (‘application’, 8) \\ (‘useful’, 60) & (‘reach’, 11) & (‘write’, 7) \\ (‘use’, 57) & (‘room’, 11) & (‘reservations’, 7) \\ (‘find’, 50) & (‘service’, 11) & (‘looking’, 6) \\ (‘good’, 47) & (‘search’, 11) & (‘abroad’, 6) \\ (‘thank’, 46) & (‘use’, 10) & (‘prices’, 6) \\ (‘reliable’, 46) & (‘problem’, 10) & (‘plane’, 6) \\ (‘accommodation’, 41) & (‘customer’, 10) & (‘Booking’, 5) \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Booking.com word frequency list. Figure 2 shows the positive word clouds for both Booking.com and Expedia, revealing that terms associated with customer satisfaction appear frequently in user comments. When comparing feedback from these international travel and planning companies, words such as \"app\", \"hotel\", \"booking\", \"use\", \"great\", and \"easy\" prominently feature for both brands, indicating that consumers are pleased with their choice of application. Additionally, the recurring mention of \"hotel\" highlights that both brands successfully meet customer expectations in presenting accommodation options. Terms like \"useful\" and \"easy\" suggest that users perceive the applications as beneficial tools, while \"great\" reflects positive emotions related to service quality, customer relations, and overall user experience. Notably, distinctions in positive terminology provide insights into consumer behaviour; for instance, high-frequency words for Expedia, such as \"room\", \"reservation\", \"travel\", \"trip\", and \"flight\", indicate the brand's effectiveness in guiding consumers through the buying process, while \"price\" and \"refund\" suggest favourable perceptions of value and return policies.