## 5 Numerical Results  
In this study, six forecasting models were used to predict hotel room occupancy rates. The long short-term memory network model used in this study contained 64 neurons. The time step, batch size, and epoch were 1, 72, and 100, respectively. For the structure of backpropagation neural networks, one hidden layer with 10 hidden nodes was used. Table 6 lists the parameters used by the forecasting models in this study. Two indices--mean absolute percentage error (MAPE) and root mean square error (RMSE), expressed as Equations (9) and (10)--were employed to measure the performance of the forecasting models. Table 7 shows two measurements of forecasting accuracy generated by six models with three datasets. In terms of forecasting accuracy, it was indicated that long short-term memory network models could generate superior forecasting accuracy compared with the other five forecasting models. According to the numerical results in Table 7, it can be observed that using sentiment analysis of review text and customers' rating scores in forecasting models can yield more accurate results than using unique data of sentiment \begin{table}
\begin{tabular}{c c c c c c c c c c c c c c} \hline \hline \(\mathbf{X_{1}}\) & \(\mathbf{X_{2}}\) & \(\mathbf{X_{3}}\) & \(\mathbf{X_{4}}\) & \(\mathbf{X_{5}}\) & \(\mathbf{X_{6}}\) & \(\mathbf{X_{7}}\) & \(\mathbf{X_{8}}\) & \(\mathbf{X_{9}}\) & \(\mathbf{X_{10}}\) & \(\mathbf{X_{11}}\) & \(\mathbf{X_{12}}\) & \(\mathbf{X_{13}}\) & \(\mathbf{Y}\) (\%) \\ \hline
3 & 3 & 1 & 2 & 3 & 2 & 7 & 0 & 2 & 0 & 1 & 3 & 5 & 56.23 \\
5 & 10 & 3 & 1 & 4 & 6 & 20 & 5 & 4 & 2 & 2 & 3 & 15 & 86.20 \\
2 & 5 & 0 & 1 & 2 & 3 & 11 & 1 & 1 & 0 & 1 & 1 & 11 & 45.13 \\
5 & 7 & 3 & 1 & 4 & 5 & 12 & 4 & 7 & 1 & 2 & 1 & 9 & 64.90 \\
2 & 2 & 1 & 0 & 2 & 1 & 6 & 2 & 2 & 0 & 1 & 1 & 4 & 67.35 \\
0 & 1 & 0 & 0 & 1 & 0 & 1 & 0 & 0 & 2 & 0 & 2 & 0 & 11.96 \\
3 & 4 & 2 & 0 & 2 & 4 & 14 & 3 & 5 & 1 & 2 & 3 & 9 & 74.91 \\
0 & 2 & 0 & 0 & 0 & 2 & 1 & 0 & 0 & 0 & 0 & 2 & 3 & 77.18 \\
4 & 7 & 3 & 1 & 2 & 4 & 14 & 1 & 3 & 0 & 8 & 5 & 8 & 60.81 \\
5 & 7 & 5 & 0 & 8 & 8 & 16 & 3 & 4 & 5 & 8 & 2 & 13 & 66.00 \\
1 & 2 & 0 & 1 & 0 & 0 & 5 & 3 & 0 & 1 & 0 & 0 & 2 & 19.31 \\ \hline \hline \end{tabular} \(\text{Y}\) (%) = monthly occupancy rates.  
\end{table} 
Table 4: All independent variables and monthly occupancy rates. Figure 6: The modeling dataset and the testing dataset. analysis of review text or customers' rating scores only. Thus, in this study, hybrid data were recommended for hotel room occupancy forecasting. Figures 7-9 plot the absolute error values of hotel occupancies provided by the forecasting models with three datasets. A boxplot was used to plot the error values. The boxplot provides a clear data distribution map when there is a large number of values in the visual representation [38]. \[\text{MAPE}(\%)=\frac{100}{\text{N}}\sum_{\text{t=1}}^{\text{N}}\left|\frac{ \text{Y}_{\text{t}}-\text{F}_{\text{t}}}{\text{Y}_{\text{t}}}\right| \tag{9}\]  
\[\text{RMSE}=\sqrt{\frac{\sum_{\text{t=1}}^{\text{N}}(\text{Y}_{\text{t}}- \text{F}_{\text{t}})^{2}}{\text{N}}} \tag{10}\]  
where N is the number of forecasting periods, Yt is the real value at period t, and Ft is the forecasting value at period t. In addition, the Wilcoxon signed-rank test [39] was performed to examine the significance of the accuracy improvement of the LSTM models. Table 8 illustrates the results of the Wilcoxon signed-rank test with \(\alpha\) = 0.025. The results indicated that the LSTM models statistically and significantly outperformed the other five forecasting models.