Tourist attraction Recommendation and service Optimization Management based on Text Big Data Mining

Abstract

Relevance of technology in tourism is increasing daily as the sector becomes more and more important to the prosperity of many economies worldwide. The quantity of meaningful data is growing along with the significance and appeal of tourism. Millions of individuals post their thoughts, ideas, and opinions about lodging, services, and much more on a daily basis on a variety of websites. The aim of this research is to propose novel method in tourist attraction based recommendation system with optimization using big data in text mining and machine learning model. here the input is collected as online review text data based in tourist attraction and processed for noise removal and normalization. Then this text features has been selected using fuzzy K clustering with graph neural networks and the selected features has been classified for emotion detection in tourist attraction recommendation using transfer attention Q-learning model. Experimental analysis has been carried out based on various online tourist recommendation text data in terms of training accuracy, precision, recall, RMSE  and F-1 score. proposed technique obtained 97% of Training   accuracy, 95% of Precision, 96% of Recall, 55% of RMSE, 93% of F-1 SCORE.