Perceptions of the Yangtze River cruise ship brand based on text semantic analysis: A web big data perspective

Authors

  • Helong Tong College of Foreign Languages, Shanghai Maritime University, Shanghai, China
  • Xian Yu College of Foreign Languages, Shanghai Maritime University, Shanghai, China
  • Wenjing Xu School of Economics & Management, Shanghai Maritime University, Shanghai, China
  • Hua Li School of Economics & Management, Shanghai Maritime University, Shanghai, China

DOI:

https://doi.org/10.33175/mtr.2022.256486

Keywords:

Yangtze River, Cruise ship brand, Web big data, Text semantic analysis

Abstract

Relying on the rich natural and human resources of the Yangtze River, the Yangtze River cruise has been a popular and famous tourism route. This study adopts a big data approach to study the perception of and satisfaction with the Yangtze River cruise brand, so as to come up with some practical suggestions for the enhancement of its value. Based on the analysis of the current development of the Yangtze River cruise tour, this paper conducted a textual analysis on 2,260 user reviews of two important Chinese travel OTA companies (Ctrip.com and Tuniu.com) through the theory and method of web text mining. A word frequency analysis, the construction of semantic networks, and a sentiment analysis were carried out. The results of the study show that the market perception of each cruise company’s brand is not very different, and the relevant companies have not yet developed their own special products and services. “Service” is the most frequently mentioned high-frequency word in tourists’ reviews. The sentiment analysis shows that the frequency of positive emotions in tourists’ reviews is high (exceeding 80 %). Through the satisfaction analysis of six dimensions of Yangtze River cruises, it is found that “cruise dining” and “cruise entertainment” are weak points.

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Published

2022-03-10

How to Cite

Tong, H., Yu, X. ., Xu, W., & Li, H. (2022). Perceptions of the Yangtze River cruise ship brand based on text semantic analysis: A web big data perspective. Maritime Technology and Research, 4(3), 256486. https://doi.org/10.33175/mtr.2022.256486

Issue

Vol. 4 No. 3 (2022): July - September

Section

Article

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