张川
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Personal Information
Supervisor of Master's Candidates
Supervisor of Doctorate Candidates
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E-Mail:
79cbb48ad07cf3cfb8ef5a327e9e836538b141e8913c69a585512826f8d37548f7440370391fa99bf8709bcd0433db27ff87cfb9d0fdf3963672c06c370375604e8953f1f8a02d437deaa1b9521a8a8e8c22723763082c81434da9931219948ef74cf26087ae05114374f66029529f8bb0bf164116ad3da962f93aa615afdbff -
Education Level:
With Certificate of Graduation for Doctorate Study -
Business Address:
文管学馆B座427 -
Contact Information:
13940180027 -
Degree:
博士 -
Professional Title:
Professor -
Alma Mater:
东北大学 -
Teacher College:
工商管理学院
Other Contact Information
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ZipCode:
034b18146fece107919ddbb491e06271b4611520e6c92c84072721fbbb6c72418fdafe91dd1f7e2e5cd1511594be4d334e0875b336d0ec0738adf79986e1457cc8fcefb6a2f394b90fe09ca8c09249adf992913790d392279b698cd0717b765897cbe2336b08b4770d626737949bf6cb6a7d5673cea8676b2a9058f88d359a28 -
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Email:
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Paper Publications
Current position: Home > Scientific Research > Paper Publications
Utilizing textual data from online reviews for daily tourism demand forecasting: A deep learning approach leveraging word embedding techniques
- Release time:2025-04-10
- Hits:
Journal:
Expert Systems with ApplicationsVolume:
260DOI number:
10.1016/j.eswa.2024.125439Abstract:
Accurately estimating daily tourism volumes is crucial for optimizing operational strategies and enhancing visitor experiences at tourist destinations. In this study, we leverage historical tourism volume data, search engine data, and online review data (including textual review contents) to forecast daily tourism demand. We develop a deep learning framework that includes a feature selection module to select search engine indices, a word embedding module to transform review texts into numerical predictors, and an extreme learning machine (ELM) enhanced with the whale optimization algorithm (WOA) for predictive modeling. Based on different word embedding techniques, we investigated two specific forecasting methods: one based on term frequency-inverse document frequency (TF-IDF) and Transformer, and the other based on the standard pre-trained bidirectional encoder representations from Transformers (BERT). These two methods enhance predictive accuracy while ensuring fast training and inference speeds, making it suitable for the high-frequency daily tourism forecasting task. Experimental results demonstrate that the two methods significantly outperform traditional approaches that rely solely on numerical data sources, achieving lower prediction errors and faster inference speeds compared to established benchmark methods, highlighting their potential to contribute to advancements in tourism prediction methodologies.Key Words:
Tourism forecasting Word embedding Deep learning Transformer BERT Textual dataIndexed by:
SCI JCR Q1Note:
SCI Q1、中科院1区、ABS 1Document Code:
125439Discipline:
Management ScienceFirst-Level Discipline:
Management Science and EngineeringISSN No.:
0957-4174Translation or Not:
no