| id |
caadria2024_68 |
| authors |
Chen, Changyu, Yu, Hanting and Guo, Yuhan |
| year |
2024 |
| title |
Perception and Reality: Urban Green Space Analysis Using Language Model-Based Social Media Insights. A Case Study within Shanghai’s Inner Ring |
| source |
Nicole Gardner, Christiane M. Herr, Likai Wang, Hirano Toshiki, Sumbul Ahmad Khan (eds.), ACCELERATED DESIGN - Proceedings of the 29th CAADRIA Conference, Singapore, 20-26 April 2024, Volume 2, pp. 119–128 |
| doi |
https://doi.org/10.52842/conf.caadria.2024.2.119
|
| summary |
As urbanization accelerates, urban green spaces play an increasingly integral role in daily life. The development of large language models (LLM) provides a practical method for assessing how people perceive green spaces. This study employs prompt-turning techniques to analyze social media data, aiming to uncover public sentiments and correlate them with the actual conditions of urban green spaces. By categorizing evaluative dimensions — location, landscape quality, facility levels, and management — diverse areas of public focus are revealed. We utilize both dictionary-based and language model-based methods for the analysis of overall emotional perception and dimensional emotional perception. Classification of sentiments into positive, neutral, and negative categories enhances our understanding of the public's general emotional inclinations. Using various spatial analysis techniques, the study delves into the current conditions of green spaces across these evaluative dimensions. In conclusion, a correlation analysis exposes patterns and disparities in these evaluative dimensions, providing valuable insights into understanding public emotional tendencies and offering effective recommendations from the perspective of public perception. |
| keywords |
Urban green spaces, Social media analysis, Public Sentiment, Spatial analysis, Prompt-turning |
| series |
CAADRIA |
| email |
|
| full text |
 file.pdf (1,805,389 bytes) |
| references |
Content-type: text/plain
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| last changed |
2024/11/17 22:05 |
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