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Detecting Novelty Seeking From Online Travel Reviews: A Deep Learning Approach

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成果类型:
期刊论文
作者:
Chen, Ting;Duan, Yaoqing;Ahmad, Farhan;Liu, Yuming
通讯作者:
Liu, YM
作者机构:
[Liu, Yuming; Chen, Ting; Duan, Yaoqing; Liu, YM] Cent China Normal Univ, Sch Informat Management, Wuhan 430079, Peoples R China.
[Ahmad, Farhan; Chen, Ting] Univ Turku, Turku Sch Econ, Turku 20540, Finland.
通讯机构:
[Liu, YM ] C
Cent China Normal Univ, Sch Informat Management, Wuhan 430079, Peoples R China.
语种:
英文
关键词:
Deep learning;Text categorization;Text recognition;Tourism industry;Social networking (online);Convolutional neural networks;Task analysis;BERT-BiGRU;novelty seeking;online travel reviews
期刊:
IEEE ACCESS
ISSN:
2169-3536
年:
2023
卷:
11
页码:
43869-43881
基金类别:
10.13039/501100004543-China Scholarship Council (Grant Number: 202206770003)
机构署名:
本校为第一且通讯机构
院系归属:
信息管理学院
摘要:
Travel online reviews is important experience related information for understanding an inherent personality trait, novelty seeking (NS), which influences tourism motivation and the choice of tourism destinations. Manual classification of these reviews is challenging due to their high volume and unstructured nature. This paper aims to develop a classification framework and deep learning model to overcome these limitations. A multi-dimensional classification framework was created for NS personality trait that includes four dimensions synthesized from prior literature: relaxation seeking, experie...

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