The impact of latent topic valence of online reviews on purchase intention for the accommodation industry

Abstract

This study utilizes machine learning (ML) natural language processing (NLP) algorithms and statistical methods in order to measure the impact that qualitative textual reviews have on booking intentions for accommodations. Using over 400,000 online reviews from 1256 accommodations in South Korea, latent Dirichlet allocation (LDA) is used to determine the topic of the review content, convolutional neural networks (CNNs) are used to identify the valence of the reviews, and spatial probit models are used to determine the impact of the review content and valence on booking intention, while controlling for several other variables. It is found that positive reviews about an accommodation’s ambiance, value, service, front office, accessibility, surrounding neighborhood and room capacity result in significantly higher booking intentions, while negative reviews in the service, front office and surrounding neighborhood result in lower probability of booking. A number of explanatory variables also have varying effects on booking intentions.