Capturing and analyzing e-WOM for travel products: a method based on sentiment analysis and stochastic dominance

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.

Customer-Oriented Strategic Planning for Hotel Competitiveness Improvement Based on Online Reviews

“…First, the adjectives, verbs and adverbs in online review information FS g ij are extracted to construct an opinion word set of online review information [76,78]. Let…”

“…In the case that some words in W j may belong to neither W + HN nor W − HN , or if the sentiment polarity of some words cannot be identified by the above method, those words are manually added to W + j or W − j based on domain knowledge [38]. Then, five-granularity sentiment analysis is carried out to obtain a more detailed evaluation of the service attributes [77,78]. Let v g ij denote the sentiment strength value of online review information FS g ij .…”