An Online Hotel Selection Method With Three-Dimensional Analysis of Reviews’ Helpfulness

被引:1
作者
Liu Y. [1 ]
Li J. [1 ]
机构
[1] Shanghai Maritime University, China
关键词
CoCoSo; Hotel Selection; Multi-Attribute Decision-Making; Online Reviews; Review Helpfulness; RFM;
D O I
10.4018/IJFSA.343490
中图分类号
学科分类号
摘要
The multi-attribute decision-making method based on online reviews has been widely used in addressing the hotel selection problem. However, due to information overload and the presence of fake reviews, traditional hotel selection methods that rely solely on unverified review analysis can affect the outcome of hotel selections. In this study, a novel three-dimensional helpful review analysis model based multi-attribute decision-making approach for hotel selection is established. Firstly, a new three-dimensional helpful review analysis model that effectively filters out sentiment inconsistency reviews, topic inconsistency reviews, and reviews from invalid sources is proposed. Secondly, this study employs TF-IDF and LDA to extract attributes for hotel selection. We further utilize BERT to extract sentiment level for each attribute. Then, a ranking result for alternative hotels is obtained using a combination compromise solution method (CoCoSo). Finally, we demonstrate its effectiveness and feasibility through a case study of selecting the optimal hotel from TripAdvisor.com. © 2024 IGI Global. All rights reserved.
引用
收藏
页码:1 / 25
页数:24
相关论文
共 50 条
[21]   A Hotel Ranking Method Through Online Reviews Considering the Cultural Background of Reviewers [J].
Shi, Jing ;
Du, Xiaoyi ;
Wu, Jian ;
Liu, Yujia .
International Journal of Fuzzy System Applications, 2025, 14 (01)
[22]   Multi-stage multi-attribute decision making method based on online reviews for hotel selection considering the aspirations with different development speeds [J].
Zhang, Chen-xi ;
Zhao, Meng ;
Cai, Ming-yao ;
Xiao, Qi-rui .
COMPUTERS & INDUSTRIAL ENGINEERING, 2020, 143
[23]   Does hotel attributes impact customer satisfaction: A sentiment analysis of online reviews [J].
Gunasekar, Sangeetha ;
Sudhakar, Sooriya .
JOURNAL OF GLOBAL SCHOLARS OF MARKETING SCIENCE, 2019, 29 (02) :180-195
[24]   Hotel customer segmentation and sentiment analysis through online reviews: an analysis of selected European markets [J].
Oliveira, Anderson S. ;
Renda, Ana, I ;
Correia, Marisol B. ;
Antonio, Nuno .
TOURISM & MANAGEMENT STUDIES, 2022, 18 (01) :29-40
[25]   A novel method based on knowledge adoption model and non-kernel SVM for predicting the helpfulness of online reviews [J].
Luo, Jian ;
Zhang, Yan ;
Gao, Yuanyuan ;
Zhang, Jing .
JOURNAL OF THE OPERATIONAL RESEARCH SOCIETY, 2024, 75 (06) :1205-1222
[26]   Sentimental feature selection for sentiment analysis of Chinese online reviews [J].
Zheng, Lijuan ;
Wang, Hongwei ;
Gao, Song .
INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS, 2018, 9 (01) :75-84
[27]   Sentimental feature selection for sentiment analysis of Chinese online reviews [J].
Lijuan Zheng ;
Hongwei Wang ;
Song Gao .
International Journal of Machine Learning and Cybernetics, 2018, 9 :75-84
[28]   LGBTQ hotel selection criteria: a thematic analysis method [J].
Herjanto, Halimin ;
Garza, Regina Falcon ;
Amin, Muslim .
JOURNAL OF HOSPITALITY AND TOURISM INSIGHTS, 2024, 7 (04) :2199-2215
[29]   The interactive effects of online reviews on the determinants of Swiss hotel performance: A neural network analysis [J].
Phillips, Paul ;
Zigan, Krystin ;
Santos Silva, Maria Manuela ;
Schegg, Roland .
TOURISM MANAGEMENT, 2015, 50 :130-141
[30]   A Hotel Ranking Model Through Online Reviews with Aspect-Based Sentiment Analysis [J].
You, Tian-Hui ;
Tao, Ling-Ling ;
Cambria, Erik .
INTERNATIONAL JOURNAL OF INFORMATION TECHNOLOGY & DECISION MAKING, 2023, 22 (01) :89-113