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An Analysis on the Structural Relationship Between Destination Personality and Traveler Rating with Word2Vec Technique based on Online Review Big Data

Shim, Yeong-Seok 1 Kim, Hong-bumm 2

1
2세종대학교

Excellent Accredited

ABSTRACT

The current study examines destination personality in a contextual semantic approach by utilizing word embedding with deep learning via Word2Vec, a neural network language model using collected online review data from Tripadvisor.com. This study tested the relationship between destination personality and traveler rating. The results show that the traits of destination personality reflect expressed affective emotion after travelers' experience, unlike existing brand personality scales that measure tangible product. Furthermore, sophistication, which is one of the dimensions of destination personality, had the most significant positive impact on the traveler rating, but ruggedness, another dimension of destination personality, appeared to have a negative effect on the traveler rating. Finally, comparing the result of the WLS regression to the OLS regression, R-squared for WLS was found to be substantially superior to that of OLS when estimating relationship by quantifying textual data.

Citation status

* References for papers published after 2022 are currently being built.

This paper was written with support from the National Research Foundation of Korea.