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

Abstract

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.

An Innovative Mobile Application for Wellness Tourism Destination Competitiveness Assessment: The Research and Development Approach

“…However, enhancement of the prediction accuracy test for machine learning requires further investigation by adopting a variety of techniques and methods to generate more accurate prediction results using the three classification techniques recommended by Yuensuk et al [24], including Naïve Bayes, Neural Network, and K-Nearest Neighbour, to develop a predictive model for classifying wellness tourism destination competitiveness level.…”

“…Innovation is not just about creating something new; it should improve current technology to uphold the foundation of a nation's community. Previous studies focused on technological application development to support growth in the context of specific areas related to wellness tourism such as a digital platform-mediated tourism system for self-service information support in small-town destinations [23], mobile applications for information system management of agrotourism activities and attractions [24], applications recommending cultural tourism activities [25], mobile applications to strengthen the relationship among agents of local communities/entities and promote mediation mechanisms among all stakeholders [26], Travel Assist, a mobile app for visitors [27] and an intelligent system that integrated data analysis over a mobile cloud IoT computing platform to promote the rural leisure tourism industry [28]. However, limited literature is available concerning the development of innovative mobile applications for wellness tourism destination competitiveness assessment and classifying the competitiveness level of wellness tourism destinations for accurate and reliable decision-making.…”

“…Further studies are required to improve the development of information systems for the effective management of agro-tourist attractions and activities to better assist tourists in decision-making and tailoring tour programs to individual needs. Yuensuk et al [24] developed an application recommending cultural tourism activities using machine learning technology. Their research methodology involved collecting data from Facebook conversations of 385 tourists who traveled to a famous tourist destination in Maha Sarakham Province.…”

Multi-Level Text Clustering in Subject Knowledge Library and its Visualization

“…For this reason, the creation and management of subject knowledge libraries have become particularly important [9][10][11][12][13][14]. The main task of a subject knowledge library is to collect large amounts of disorderly scattered subject information so that users can search for and utilize this subject information easily through reasonable organization and classification.…”

A New Feature-Based Method for Similarity Measurement under the Linux Operating System

“…Semantic word similarity estimation is an important component that plays a crucial role in text processing and understanding. It has been included in a wide range of natural language processing tasks intending to make them seem more intelligent [1] such as ontology learning, information retrieval, question answering, word sense disambiguation, text classification ,text summarization, electronic learning, and machine translation [2,3,4,5]. The computational methods of semantic similarity are used for identifying and quantifying likeness between pair of words by exploiting the common attributes shared between them.…”