Information Systems for Cultural Tourism Management Using Text Analytics and Data Mining Techniques

Information Systems for Cultural Tourism Management Using Text Analytics and Data Mining Techniques

Cultural Tourism Management

Opinion Data Mining

Text Mining

Tourist Attraction

Tourist Experience

Using technology to deliver specific human interests is gaining attention. It results in humans being presented differently with what each individual wants. Therefore, this research aims to develop a culturally tourism recommended application using machine learning technology. It has three objectives: to develop a predictive model for cultural tourism management using text mining techniques, to evaluate the effectiveness of the cultural tourist attraction management model, and to assess the satisfaction of using the application for cultural tourism management. The research data was collected on Facebook conversations from 385 tourists (3,257 transactions) who had traveled to a famous tourist destination in Maha Sarakham Province. The prediction model development tools are three classification techniques including Naïve Bayes, Neural Network, and K-Nearest Neighbor. The model performance evaluation tool consists of a confusion matrix and cross-validation methods. In addition, a questionnaire was used to assess the satisfaction of the application. The results showed that the model with the highest accuracy was modeled by the Naïve Bayes technique with an accuracy of 91.65%. Simultaneously, the level of satisfaction with the application was high, with an average of satisfaction equal to 3.98 (S.D. equal to 0.69). It was therefore concluded that the application was accepted by it to be further expanded to offer more widespread research.

Yuensuk , Thanet

Limpinan , Potsirin

Nuankaew , Wongpanya

Nuankaew, Pratya

International Journal of Interactive Mobile Technologies (iJIM); Vol. 16 No. 09 (2022); pp. 146-163

1865-7923

International Association of Online Engineering (IAOE), Vienna, Austria

2022-05-10

Copyright (c) 2022 Thanet Yuensuk , Potsirin Limpinan , Wongpanya Sararat Nuankaew , Pratya Nuankaew

https://creativecommons.org/licenses/by/4.0

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eng

info:eu-repo/semantics/article

info:eu-repo/semantics/publishedVersion

Peer-reviewed Article