Trend analysis of online travel review text mining over time. (4th December 2019)

• Record Type: Journal Article
• Authors: Zhang, Kaile; Koshijima, Ichiro
• Is Part Of: Journal of modelling in management. Volume 15:Number 2(2020)
• Page Start: 491
• Page End: 508
• Publication Date: 2019-12-04
• Subjects: Information systems -- Management -- Management information systems -- Optimization -- online travel -- Reviews context mining -- Latent semantic analysis -- Multidimensional scaling; Industrial management -- Mathematical models -- Periodicals; Industrial management -- Computer simulation -- Periodicals; Business -- Mathematical models -- Periodicals; Business -- Computer simulation -- Periodicals; 658.4033
• DOI: 10.1108/JM2-10-2018-0178
• Languages: English
• ISSNs: 1746-5664

Abstract

Abstract : Purpose: The reviews of online tourism have not been taken advantage of effectively because the text data of such reviews is enormous and its current, in-depth research is still in infancy. Therefore, it is expected that the text data could be processed by the method of text mining to better understand the implicit information. The purpose of this paper is to contribute to tourism practitioners and tourists to conveniently use the texts through appropriate visualization processing techniques. In particular, time-changing reviews can be used to reflect the changes in tourists' feedback and concerns. Design/methodology/approach: Latent semantic analysis is a new branch of semantics. Every term in the document can be regarded as a single point in multi-dimensional space. When a document with semantics comes into such space, the distribution of the document is not random, but will obey some type of semantic structure. Findings: First, overall grasping for the big data is applicable. Second, propose a direct method is proposed that allows more non-language processing researchers or proprietors to use the data. Lastly, the results of changes in different spans of times are investigated. Originality/value: This paper proposes an approach to disclose a significant number of travel comments from different years that may generate new ideas for tourism. The authors put forward a processing approach to deal with large amounts of texts of comments. Using the case study of Mt. Lushan, the various changes of travel reviews over the years are successfully visualized and displayed.