Sentiment and Topic Analysis of Online Reviews Based on the BERT-LDA Model: Informing Sustainable Development of Urban Lighting Landscapes

As night tourism has become a key driver of the nighttime economy in Chinese cities, urban lighting landscapes—as core attractions for night tourism—call for systematic investigation into the relationship between their development effectiveness and visitor experience. However, existing studies have primarily focused on design strategies or economic value, lacking in-depth exploration of visitor experience based on large-scale online review data. Furthermore, the integrated analysis of sentiment analysis and topic modeling for online reviews remains insufficient, making it difficult to uncover the dimensional differences underlying different sentiment types. Therefore, this study employs a hybrid BERT-LDA model to conduct a collaborative analysis of 45,768 valid tourism online reviews covering 30 urban lighting landscapes in China. First, the pre-trained Bidirectional Encoder Representations from Transformers (BERT) model identified the sentiment orientation of each review. Subsequently, the Latent Dirichlet Allocation (LDA) model extracted latent topics from positive and negative reviews separately. Research findings indicate that tourists generally hold positive attitudes toward urban lighting landscapes. Specifically, five positive topics and three negative topics were extracted from the review corpus. This study fills a research gap in the field of urban lighting landscapes and reveals the underlying demands of Chinese tourists regarding such landscapes from a demand-side perspective, thereby providing a decision-making basis for optimizing the planning and design of urban lighting landscapes.