Research On Tourist Flow Forecasting Based On Text And Web Search Information

With the advent of the 5G era,more and more tourists use the Internet to understand tourist destinations in advance and then make travel plans.Based on these Internet data information,the accuracy of forecasting the number of tourists can be greatly improved,which can not only dynamically monitor the behavior of tourists,but also overcome the time lag problem of traditional tourism data.Therefore,it is necessary to predict tourist traffic based on Internet data.Based on the research of existing literature,this paper takes the monthly tourist flow of Hainan Province as an example to carry out the empirical research of tourism forecasting,and the main research work is as follows:(1)In view of the characteristics of multiple noise,nonlinearity and high fluctuation of network search information,there are many difficulties in the selection of keywords and index synthesis.This paper proposes a new search keyword selection and exponential synthesis technology,R/S-TDC-EMD-KPCA method,which first uses the rescale range method(R/S)and time difference correlation method(TDC)to select keywords with predictive ability,performs empirical mode decomposition(EMD)noise reduction on the search volume of selected keywords,and finally synthesizes the network comprehensive search index by nuclear principal component(KPCA)method.The effectiveness of the extracted web comprehensive search index in tourist flow prediction is verified by comparison.(2)Internet data information represents the different behavioral characteristics of tourists,which can fully reflect tourists’ concerns,interests and emotional tendencies.This paper proposes a new method of tourism forecasting based on the integration of Internet data such as Baidu index and Weibo text.Firstly,based on the R/S-TDC-KPCA method,the Baidu index is synthesized into a comprehensive online search index.Secondly,the text data information related to the optimal keywords is extracted from the mainstream Chinese social platform Sina Weibo,the extracted text information is cleansed,and the emotion index is constructed by using the methods of simple addition of positive and negative emotions and asymmetry based on positive and negative emotions.Finally,the comprehensive search index,sentiment index and historical tourist flow are used as input variables,and the SARIMAX model is constructed for empirical prediction research.The empirical results show that compared with other traditional prediction models,the network comprehensive search index based on R/STDC-EMD-KPCA method combined with BP neural network has a lower mean absolute percentage error(MAPE)and normalized root mean square error(NRMSE)in Hainan tourism forecasting,in which MAPE decreases from 10.44% to 7.11%,and NRMSE decreases from 14.66 to 9.81.Therefore,the proposed R/S-TDC-EMD-KPCA method can extract and synthesize web search information with high quality,and then can be effectively used for auxiliary prediction of tourist flow.Secondly,it is found that when the network comprehensive search index and Weibo sentiment index are used as predictors at the same time,the prediction accuracy can be effectively improved,and the horizontal prediction accuracy is lower than that of other benchmark models,MAE drops to15.23,MAPE drops to 2.62%,RMSE drops to 21.77,and RMSPE drops to 3.47%.In addition,this paper adopts two different methods to compile the sentiment index,namely the emotion index based on the simple addition of positive and negative emotions and the emotion index based on the asymmetric emotion of positive and negative emotions,and it is found that different emotion index compilation methods will have a certain impact on the prediction results.The emotional index under the asymmetric situation of positive and negative emotions based on different human psychological behaviors can better reflect the emotional tendency of tourists and obtain better prediction effect than the simple sum of positive and negative emotions.Therefore,the prediction of tourist flow based on text and web search information is effective,which provides a new way to accurately predict tourism demand.