Use of Natural Language Processing to understand users’ perspective on the Art Places/Places of Interest in Global Cities Singapore and Hong Kong

Abstract

The Art Places/Places of Interest (POI) are increasingly important for Singapore and Hong Kong in their bid to be Global Cities. The design and operation of such Art Places are often led by the museum owners, city government and panel of experts from a top down approach, as well as funded by national governments for public benefit as part of long-term planning. The inputs from the actual users are often neglected. Public participation in Art Places/POI is often limited by individual visits and interactions. The diverse views and feedback on the design and operation of Art Places are difficult to capture accurately. In order to understand the perceptions of the users, extensive and expensive surveys and interviews need to be undertaken. Despite this, there is still a challenge of selection bias and interpretation bias. This paper explores the use of technology and big data to understand the similarities and differences between well-liked and disappointing areas of Art Places/POI in Singapore and Hong Kong. Public reviews on Art Places/POI in Singapore and Hong Kong will be examined using Natural Language Processing tools including the prevalent topic modelling method, namely Latent Dirichlet Allocation. The study revealed common strengths and weaknesses among artistic venues in Singapore and Hong Kong. “Place and experience” emerged as a common strength, while “price and content” were identified as a shared weakness. Singapore’s Art Places were distinguished by a unique strength in their “kid-friendly element,” whereas Hong Kong excelled in “food and shopping.” However, Singapore faced a unique weakness in “racial enclaves,” whereas Hong Kong’s distinctive weakness lay in “service.” These insights can aid urban planners and operators in comprehending and addressing areas of improvement highlighted by negative reviews, thereby enhancing overall performance.