## 6 Conclusions  
Because of the expansion of online travel agent booking systems, the collection of customers' opinions on accommodations has become more diversified. Customers' online rating scores and review text in booking systems have become major sources that gather customers' experiences; hence, they are essential influences to forecast hotel occupancy. This study used three datasets--a sentiment analysis of review text, customers' rating scores, and combined data of customers' rating scores--to predict the monthly hotel occupancy rates with a one-month-ahead forecasting policy. Besides, multilingual comment contents were included in this study. Six forecasting models--LSTM, BPNN, GRNN, LSSVR, RF, and GPR--were utilized to deal with the same dataset. The numerical results indicated that the long short-term memory network model could provide more accurate hotel occupancy forecasting rates than the other five models in three datasets. In addition, the hybrid data of the sentiment analysis of review text and customers' rating scores produced the most accurate results among all forecasting models.  
For future studies, more detailed data, such as attributes of hotels and customers, can be added as input data for forecasting models. The hotel attributes include international type, customer type, location, the number of rooms, and prices. The customer attributes possibly consist of gender, nationality, and stay type. The sentiment analysis and comparison according to clusters of different countries and the study of site-specific peculiarities could be paths for future study. With more detailed data, forecasting models are able to provide more informative data for hotel management and decision-making. In addition, the hotel room occupancy used in this study was gathered from public sectors that do not provide bed and breakfast room occupancy data. Bed and breakfast room occupancy data, mostly used for the AirBnB system, could be another possible direction for further study to examine the performance of the proposed hotel room occupancy forecasting architecture. Finally, because the COVID-19 crisis affected hotel room occupancy, the data collected in the COVID-19 period is a challenging topic worthy of future investigation.  
Conceptualization, P.-F.P.; data curation; Y.-M.C., C.-H.C. and J.-P.L.; formal analysis, Y.-M.C. and P.-F.P.; methodology, Y.-M.C. and P.-F.P.; software, C.-H.C., J.-P.L. and Y.-L.L.; visualization, Y.-M.C., J.-P.L. and Y.-L.L.; writing--original draft preparation, Y.-M.C. and P.-F.P.; writing--review and editing, P.-F.P.; funding acquisition, P.-F.P. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Ministry of Science and Technology, the Republic of China, Taiwan, under the contract number MOST 109-2410-H-260-023.  
Not applicable.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Dataset**} & \multirow{2}{*}{**Measurements**} & \multicolumn{5}{c}{**LSTM vs.**} \\ \cline{3-6}  & & **LSSVR** & **BPNN** & **GRNN** & **GPR** & **RF** \\ \hline \multirow{4}{*}{Dataset I} & Z Value & 4.042 & 3.296 & 4.534 & 7.507 & 5.903 \\  & Significance & Yes & Yes & Yes & Yes & Yes \\  & Positive number & 184 & 188 & 185 & 206 & 192 \\  & Negative number & 134 & 130 & 133 & 112 & 126 \\ \hline \multirow{4}{*}{Dataset II} & Z Value & 3.821 & 3.327 & 4.204 & 5.545 & 5.315 \\  & Significance & Yes & Yes & Yes & Yes & Yes \\  & Positive number & 184 & 177 & 180 & 193 & 195 \\  & Negative number & 134 & 141 & 138 & 125 & 123 \\ \hline \multirow{4}{*}{Dataset III} & Z Value & 4.065 & 2.783 & 3.164 & 3.849 & 6.052 \\  & Significance & Yes & Yes & Yes & Yes & Yes \\ \cline{1-1}  & Positive number & 194 & 174 & 178 & 179 & 198 \\ \cline{1-1}  & Negative number & 124 & 144 & 140 & 139 & 120 \\ \hline \hline \end{tabular}
\end{table}
Table 8: The results of the Wilcoxon signed-rank test with \(\alpha=0.025\).