## 4 Results
### Sentiment Analysis Results  
In this study, the Naive Bayes method was used to classify the sentences in the data set according to their emotional states. In this probability-based method, the data set is first divided into two parts: training and testing. Training data is used to determine the parameters of the model; test data is used to evaluate the developed model. In the separation of the data set, different ratios (such as 60-40%, 70-30%, 80-20%) were tested, and the best result was obtained with a 75% training and 25% testing ratio. Accordingly,750 comments were used to train the model and 250 comments were used during the testing phase of the model. This process was carried out meticulously to increase the overall success and accuracy of the model. The metrics used to evaluate the performance of classification models play a critical role in measuring the predictive accuracy and effectiveness of the model. In this context, metrics such as precision, recall, accuracy, and F1 score (F-measure) are widely preferred for evaluating classification performance. Each of these metrics evaluates different aspects of the model and may have different degrees of importance in various application scenarios. Precision is the success rate of its predictions, that is, it shows the number of predicted positive classes (classes predicted as 1) that are positive. It focuses on the success of the predictions [54]. \[\text{Precision}=\frac{\text{TP}}{\text{TP}+\text{FP}} \tag{2}\]  
TPs (True Positives) represent the number of true positive predictions, while FPs (False Positives) represent the number of false positive predictions. Recall is the rate at which the positive class (1) is correctly predicted. It shows how many of the predicted positive classes are correctly predicted. It is an important measure because it provides information about the cost of oversights. It focuses on the success of capturing the facts [54]. \[\text{Recall}=\frac{\text{TP}}{\text{TP}+\text{FN}} \tag{3}\]  
In this formula, FN (False Negatives) represents the number of false negative predictions. Accuracy is the correct classification rate. The accuracy value is calculated by the ratio of the areas we correctly predicted in the model to the total data set. The maximum value of accuracy can be 1 [55]. \[\text{Accuracy}=\frac{\text{TP}+\text{TN}}{\text{TP}+\text{TN}+\text{FP}+ \text{FN}} \tag{4}\]  
In the formula, TN (True Negatives) represents the number of correct negative predictions, while TP, FP and FN are defined as explained above. F1 score is the harmonic mean of precision and recall values [55]. \[\text{F1}=\frac{2\times\text{Recall}\times\text{Precision}}{\text{Precision}+ \text{Recall}} \tag{5}\]  
These metrics are used to comprehensively evaluate the performance of the classification model. While precision, recall and F1 scores generally produce more meaningful results in unbalanced data sets, the accuracy metric is used to evaluate the overall success of the model. The importance of each metric varies depending on the application area and the structure of the data set [56]. Table 4 shows the comparative results of the metrics used in the performance and success evaluation of the model developed using the Naive Bayes method, one of the machine learning models. The model created using the Naive Bayes method appears to be successful in user sentiment classification for Booking.com and Expedia. The precision metrics are 0.971 and 0.959, respectively, indicating that the model classifies positive examples correctly for both cases and that there are very few false positive classifications. The recall metric of 0.983 for Booking.com shows that it successfully detects samples that are positive and that there are few false negatives. The fact that Expedia's score is 0.908, which is lower than that of Booking.com, shows that it cannot detect some of the positive samples. The accuracy metrics of 0.958 and 0.918, respectively, show that the model is more successful in the total correct classification of Booking.com than Expedia.