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Abstract

Integrating digital technologies into urban spaces has transformed traditional placemaking, with user-generated content (UGC) offering invaluable insights into public perceptions. This study addresses a significant research gap by investigating how UGC sentiment analysis can capture and inform understanding of culturally important sites, focusing on the Yogyakarta Philosophical Axis. The research aims to uncover UGC-derived insights for architectural and urban planning decisions and to examine the relationship between digital sentiments and authentic physical experiences along this UNESCO-recognized heritage corridor. Employing a robust methodology, over 1,700 Google Maps reviews from six key sites – Tugu Yogyakarta, Malioboro Street, North Square, Kraton, South Square, and Panggung Krapyak – were collected, preprocessed, and analyzed using BoardFlare, DeepSeek AI, and ChatGPT for sentiment triangulation. Findings reveal a generally positive perception of the Axis, yet expose a critical disparity between high numerical star ratings and more nuanced, often lower, textual sentiment scores, highlighting the limitations of simplistic metrics. While sites like Tugu Yogyakarta garnered high positive sentiment due to their symbolic resonance, others, such as Panggung Krapyak, received low scores due to infrastructural deficiencies and limited access, indicating significant 'placemaking inequality.' This study highlights the crucial role of multi-tool sentiment analysis in mitigating computational biases and fostering a comprehensive understanding of digital affect. It demonstrates that UGC, when critically analyzed, serves as a powerful diagnostic tool for urban heritage management, revealing how digital interactions both reflect and shape perceptions of place, thereby informing holistic strategies for balancing cultural preservation with the demands of modern urban development.

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How to Cite
Gunagama, M. G. ., Al Bareeq, M. M. ., & Akbar, F. P. . (2025). Digital Placemaking in the Yogyakarta Philosophy Axis: UGC Sentiment Analysis. Unisia, 43(1). https://doi.org/10.20885/unisia.vol43.iss1.art15