Abstract
PurposeIn recent years, electronic word-of-mouth (e-WOM) concerning travel products reflected in online review information has become an important reference for tourists to make their product purchase decisions, while for travel service providers (TSPs), monitoring and improving the e-WOM of their travel products is always an important task. Therefore, based on the online review information, how to capture e-WOM of travel products and find out specific ways to improve the e-WOM is a noteworthy research problem. The purpose of this paper is to develop a method for capturing and analyzing e-WOM toward travel products based on sentiment analysis and stochastic dominance.Design/methodology/approachSpecifically, online review information of travel products is first crawled and preprocessed. Second, sentiment strengths of online review information toward travel products concerning each feature are judged. Then, the matrix of structured online review information toward travel products is formed. Further, the matrix of e-WOM comparisons between any two travel products is constructed, and e-WOM ranking concerning each travel product is determined. Finally, trade-off chart models are constructed to conduct the e-WOM improvement analyses concerning the travel products.FindingsAn empirical study based on the online review information toward six travel products crawled from the Tuniu.com website is given to illustrate the use of the proposed method.Originality/valueThe proposed method can not only realize the real-time e-WOM monitoring to travel products but also be useful for TSPs to improve the e-WOM of their travel products.
Subject
Computer Science (miscellaneous),Social Sciences (miscellaneous),Theoretical Computer Science,Control and Systems Engineering,Engineering (miscellaneous)
Reference81 articles.
1. Electronic word-of-mouth versus word-of-mouth in the field of consumer behavior: a literature review;Journal of Critical Reviews,2020
2. Analysis of the impact of E-WOM, online reviews and information quality on purchase intentions on the Tokopedia market;Prologia,2021
3. Opinion mining and information fusion: a survey;Information Fusion,2016
4. Method for ranking products through online reviews;Journal of Systems Engineering,2018
5. Daily tourism volume forecasting for tourist attractions;Annals of Tourism Research,2020
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