Classification of Tourism Web with Modified Naïve Bayes Algorithm

Abstract:

In this paper we report results of a research aimed at classification Web contents on tourism with a modified Naïve Bayes algorithm. We used Web pages relating touristic information about Thailand. An appropriate light-weight tourism ontology with related terms was used to improve the results, which were categorized into six categories (attractions, accommodation, dining, local product markets, One Tambon One Product (OTOP) shops, and events). The Naïve Bayes algorithm generates results for each category, but Web pages can contain diverse information about tourism spanning over groups. The initial Web classification system could not categorize 130 sites (27.40%) out of 475 tested pages, because those Web pages contain words from more than one category. Therefore, we modified the Naïve Bayes algorithm to improve the efficiency of Web classification, which was then tested with the help of F-Measure: the results show 100% for precision, 97.39% for recall, and 98.58% for F-measure.

• Periodical: Advanced Materials Research (Volumes 931-932)
• Pages: 1360-1364
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.931-932.1360
• Online since: May 2014
• Authors: Naruepon Panawong, Chakkrit Snae Namahoot*, Michael Brückner
• Keywords: Classification, Information Retrieval, Naive Bayes, Tourism Ontology, Web Analysis

References

[1] K. K Sureshkumar, M. Umadevi, N.M. Elango, Divisive Clustering method using Naïve Bayes Algorithm for Text Categorization. International Journal of Advanced Research in Computer and Communication Engineering. 2: 4 (2013), 1747-1753.

[2] A. Khan, B. Baharudin, L.H. Lee, K. Khan, A Review of Machine Learning Algorithms for Text-Documents Classification, Journal of Advances in Information Technology. 1: 1 (2010) 4-20.

DOI: 10.4304/jait.1.1.4-20

[3] T.M. Nogueira, S.O. Rezende, H.A. Camargo, On The Use of Fuzzy Rules to Text Document Classification, International Conference on Hybrid Intelligent Systems. (2010) 19-24.

DOI: 10.1109/his.2010.5600076

[4] K. Chatcharaporn, T. Angskun, J. Angskun, Tourist Attraction Categorization Models using Machine Learning Techniques, Suranaree Journal of Science and Technology. 6: 2 (2011) 35-58.

[5] N. Panawong, C. Snae Namahoot, Thailand Tourism Web Clustering System using Naive Bayes Algorithm, The 9th National Conference on Computing and Information Technology. (2013) 83-89.

[6] N. Panawong, C. Snae Namahoot, Performance Analysis of an Ontology-Based Tourism Information System with ISG Algorithm and Name Variation Matching. NU Science Journal. 9: 2 (2013) 47-64.

[7] N. Panawong, C. Snae, Search System for Attractions in Thailand with Ontology and Name Matching. Journal of Information Science and Technology. 1: 2 (2010) 60-69.