Detecting Novelty Seeking from Online Travel Reviews – A Deep Learning Approach

Internet travel reviews are a valuable source of data on experience to understand the search of new products (NS),a personality trait that plays a key role in the impact of travel motivation and travel destination. However, with the excessive number of criticism and lack of their structure, order and categorize on hand is a difficult task. To solve this problem, propose to develop a pattern and deep learning classification System. Our frame includes four Dimensions linked to the traits and use a deep- large bert Learning Model to automatically detect NS of NS in TripAdvisor magazines from a set of 30,000 data. Rating, using the multi-dimensionally NS scale NS and Bert-bigger-bigger, demonstrated great precision in identifying personality trait. Has obtained impressive explanations and f1 results, exceeding other patterns of deeply learning in the process. This study highlights how callcitic approximate 444 catching methods can be automatically implemented personality traits of travel. He represents a strong painting to rank personality traits, which may be useful for marketing and Recommendations in the journey industry. Internet travel reviews provides valuable information in the innovation Search (ns), a personality trait that affects and motivations for the passengers destination. However, Volume and disorganization. These magazines make incomprehensible manual category. To solve this problem, we offers a learning frame in depths using Bert-Buck Model, which combines the context understood languages with the sequential learning abilities of the large.