Title: Analysing traveller ratings for tourist satisfaction and tourist spot recommendation

Authors: Rajeni Nagarajan; J. Angel Arul Jothi

Addresses: Department of Computer Science, Birla Institute of Technology and Science Pilani, Dubai Campus, Dubai, United Arab Emirates ' Department of Computer Science, Birla Institute of Technology and Science Pilani, Dubai Campus, Dubai, United Arab Emirates

Abstract: In this study, we propose an automated system to classify traveller ratings on travel destinations in ten categories across East Asia using the UCI travel reviews dataset. The automated system developed in this study is called traveller rating classification system (TRCS). Since the travel reviews dataset is an unlabelled dataset, K-means clustering algorithm is used to group the samples from the dataset into three clusters. The cluster numbers obtained from K-means clustering are assigned as class labels for the samples and the dataset is converted into a labelled dataset. Popular individual classifiers and ensemble classifiers are used to classify the samples present in the labelled dataset. In this study, bagging with decision tree classifier achieved the best classification accuracy of 97.95%. The study further analyses the attributes in the dataset using visualisation techniques to draw inferences by performing small transformations on them. The proposed system will be useful to understand traveller satisfaction and as a tourist spot recommendation system.

Keywords: data mining; recommender systems; k-means clustering; travel and tourism; ensemble learning; supervised learning; classification problem.

DOI: 10.1504/IJBIDM.2022.120828

International Journal of Business Intelligence and Data Mining, 2022 Vol.20 No.2, pp.208 - 234

Received: 23 Aug 2019
Accepted: 17 Jul 2020
Published online: 11 Feb 2022 *

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