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.
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