A big data analytics framework for determining the travel destination preferences of Indian tourists

Abstract

The growth of technology and social media websites has increased the potential to online explore different products and places around the globe. While online websites are primarily responsible for the generation of large amounts of data, this big data may be beneficial to other users provided the proper decision pattern can be analyzed. This work is focusing on the big data from social media to determine the travel destination preferences for Indian tourists. The analysis of online tourism reviews is beneficial to both tourists and businesses in tourist countries. Tourists can analyze all the required aspects prior to traveling and businesses in the destination country can enhance their products. The study aims to analyze the online tourist reviews using supervised machine learning methods (decision tree, k-nearest neighbor, back propagation neural networks and support vector machine) and ensemble learning in order to ascertain the travel preferences of Indian tourists visiting other countries. For the research experiments, significant travel data histories of tourists for the five destination places (Dubai, Indonesia, Malaysia, Thailand and Singapore) are extracted from TripAdvisor. TripAdvisor is a worldwide popular tourism website that provides access to consumers to share their travel experiences. From the selected five destination places, the preferences of Indian tourists are analyzed for the factors of travel & destination comfort, hotel facilities, food quality and attractions of the place. The analysis results of the proposed recommendation system indicate the determination of precise suggestions for Indian tourists traveling to other countries.