Recognition Of Novelty Seeking Personality Traits In Travel Online Reviews Based On Deep Learning

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 such as blogs,BBS or forum websites in different forms.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.It can make the recommendation more diversified and help eliminate the boredom caused by the duplication of information.At the same time,it can also improve the satisfaction of tourists.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 big data,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.In response to the above-mentioned problems,this thesis mainly conducts the following research work based on a large number of related documents:(1)Based on the actual needs of the research,the basic idea of data selection and the process of preprocessing are introduced to ensure that the selected data have a certain degree of representativeness,which can make the experimental results more convincing.Select 10 different types of popular attractions review data on the world’s leading travel website TripAdvisor for research,and perform operations such as removing stop words and punctuation on the collected data to prepare for follow-up experiments.(2)In response to the current complex problems of the novelty seeking evaluation scales,summarize the novelty seeking scales published by the predecessors.This thesis aims to summarize the characteristics of each dimension,merge the similar items of the existing measures,and add different items according to different dimensions.Then build and generate a new NS scale.On this basis,artificially summarize and generate a list of novelty seeking indicators,which provides a scientific and convenient theoretical basis for the calibration of experimental data in the training set.(3)By combing the language pre-training model BERT and the deep learning model BiGRU,a novelty seeking personality recognition model based on deep learning is proposed.Based on the novelty seeking scales,the deep learning model is selected according to novelty seeking recognition effect expected in this thesis.At the same time,a comparative experiment is designed to verify that the model selected in this thesis has a higher accuracy.(4)Propose more model application scenarios.Based on the research ideas of the deep learning-based novelty seeking recognition model proposed in this thesis,the deep learning model can also be used to recognize different personality traits.A method of combing traditional personality measurement and recognition methods with deep learning algorithms is proposed,and it is hoped that a recognition method with higher efficiency and accuracy can be obtained.