Mining of risk perception dimensions of Chinese tourists' outbound tourism based on word vector method

Abstract

Introduction: Safety is the lifeline of tourism development. The article's goal is to examin how Chinese tourists perceive risk when travelling aboard.

Methods: In order to create the initial corpus, this study first uses "outbound tourism"as the key word to crawl the question and answer (hereinafter referred to as "Q & A") data from 4 Chinese online travel operator platforms, then preprocesses the "Q & A" data in Python. Secondly, after being extracted, the feature words are converted into the word vector model using the word vector method based on neural network language model. Finally, the word vectors are clustered and classified.

Results: It is found that there are six dimensions of risk perception of Chinese tourists' outbound tourism, namely traffic risk, planning risk, service risk, communication risk, financial risk and functional risk.

Discussion: Important and practical information for government and tourism enterprises is provided to accurately grasp the risk perception of Chinese tourists' outbound tourism and continuously improve the supply of tourism risk information.

Keywords: China; mining; outbound tourism; risk perception dimensions; word vector method.