Recognition of Novelty Seeking Personality Traits in Travel Online Reviews on Deep Learning

Abstract

The rapid development of tourism Internet applications has generated a lot of comments and information related to tourist attractions. These comments reflect the tourists’ thoughts and preferences about tourist attractions after the travel experience and appear in various media. Travel online reviews are becoming an increasingly important experience information carrier for potential customers that spend a lot of time reading online reviews to assist in travel decisions. In the tourism industry, novelty seeking is a personality trait that is widely considered to be related to tourism motivation and the choice of tourist destinations. Novelty seeking not only involves many aspects of tourists’ activities such as eating, accommodation, traveling, shopping, and entertainment, but also related to tourists’ loyalty, willingness to return, and satisfaction. Therefore, it is necessary to identify the novelty seeking personality traits from the massive travel online reviews, and recommended new travel destinations based on the customer’s novelty seeking tendency. The traditional measurement and identification methods of personality traits have the limitations of being used in a wide range of audiences and the inaccuracy caused by the subjectivity of the subjects. Relying only on traditional methods to identify novelty seeking from massive online travel reviews is almost an impossible mission. In the era of data explosion, text classification methods based on bag-of-words and traditional machine learning appear to be inefficient. The rapid development of deep learning technology has brought more possibilities for the solution of text classification tasks. Research on how to combine new technologies with existing solutions and improve the accuracy of the existing schemes has high practical value.