Aspect-Based Linguistic Appraisal From Google Maps Reviews of an Indonesian Super Priority Tourism Destination: A Systemic Functional Linguistics Perspective

Abstract

Visitor reviews on products, particularly in the tourism sector, have been widely studied to determine visitor evaluations of the services provided. However, few have linked these assessments to aspects of the product or service. This study aims at linguistically describing visitor evaluations in relation to tourist destination aspects. In a quantitative and qualitative research approach, this study applied Appraisal Theory framework in Systemic Functional Linguistics to analyze Google Maps reviews of eight-month period in 2025 on the visits of Borobudur Temple, a UNESCO world heritage site, as one of the super priority tourism destinations in Indonesia. This study reveals that Attraction received the highest positive comments compared to other aspects. This is demonstrated by the frequent use of Appreciation subcategory, which focuses on the beauty and structure of Borobudur Temple. However, this positive evaluation contrasts with the numerous negative evaluations for other destination aspects related to facilities and services. Accessibility aspect is evaluated negatively due to its challenging physical layout and poor navigation of the temple. Similarly, the Price aspect is considered negative due to the expensive entrance ticket. The human resources aspect is also considered negative due to a lack of professionalism and inconsistent service. This negative sentiment is realized by the high number of negative comments in the Judgement and Appreciation subcategories. This study concludes that there is imbalance between visitors’ awe and frustration when visiting Borobudur Temple. For tourist destination management, the findings of this study offer the managers of Borobudur Temple diagnostic points to improve the quality of Borobudur Temple services. For further research, this study recommends the need for conducting product evaluation analysis by combining aspect-based linguistic framework to build a more accurate semantics-based model of sentiment analysis in the digital tourism landscape.