Classifying Hotels in Shiraz Based on Online Reviews Using the K-MEANS Clustering Method and Artificial Neural Network
Document Type : Original Article
Authors
1
Associate Professor in Information Technology Management, Faculty of Social Sciences and Economics, Alzahra University, Tehran, Iran
2
Ph.D. in Business Management, Faculty of Social Sciences and Economics, Alzahra University, Tehran, Iran
Abstract
Big data has fundamentally changed the management of the tourism and hospitality industry and the relationship between customers and businesses by simplifying the decision-making process based on the mass of data. On the other hand, classifying the market based on online reviews can help business owners, including hotel managers, know their customers and choose appropriate strategies accordingly. Therefore, data-driven approaches need to be developed so that the data collected from social media can be analyzed for market classification purposes. In this regard, the current study sought to classify hotels in Shiraz based on the satisfaction of the guests evaluated through the data derived from the TripAdvisor website using the k-means clustering method and an artificial neural network. To this end, a total of 105 hotels were classified into four clusters based on the 6175 data sets obtained from the pre-processing phase. Accordingly, Cluster 1 comprised 57 hotels, including hotels obtaining the highest scores in terms of all the features considered for evaluation. Moreover, Cluster 2 consisted of 19 hotels with high scores, being rated lower in terms of quality than those listed in Cluster 1. In addition, Cluster 3 included 23 hotels with an average score, and the remaining six hotels classified under Cluster 4 received the lowest scores. On the other hand, artificial neural network diagrams were used to show the process of predicting satisfaction within the individual clusters. The findings of the study confirm that the analysis of the big data collected from social media can be effectively evaluated via machine learning methods to be used for the development of businesses.
Keywords
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