Urban planning primarily depends on public input. However, citizen engagement is often limited by traditional communication channels. Historically, urban planning decisions have lacked mechanisms for incorporating feedback from the general public. Modern technology – particularly artificial intelligence (AI) – now offers the means to address this gap. Social media platforms, in particular, provide opportunities for broader and more dynamic interactions between citizens and planners. This study presents the design of an AI-based communication system for urban planning that gathers and analyzes citizen input in the Albanian language from social media platforms. Given the absence of existing natural language processing (NLP) tools for Albanian, the system uses a custom-built NLP pipeline, incorporating manual data preprocessing steps such as tokenization, lemmatization, and sentiment analysis. By integrating these preprocessing procedures with machine learning models for trend analysis and opinion classification, the system empowers urban planners to make data-driven decisions based on real-time public feedback. This work demonstrates the potential for additional automation and highlights the adaptability of AI techniques in addressing language-specific resource constraints.
Designing a natural language processing-driven communication system for urban planning: A case study
Shehu, Dhurata
;Kyratsis, Panagiotis
2025
Abstract
Urban planning primarily depends on public input. However, citizen engagement is often limited by traditional communication channels. Historically, urban planning decisions have lacked mechanisms for incorporating feedback from the general public. Modern technology – particularly artificial intelligence (AI) – now offers the means to address this gap. Social media platforms, in particular, provide opportunities for broader and more dynamic interactions between citizens and planners. This study presents the design of an AI-based communication system for urban planning that gathers and analyzes citizen input in the Albanian language from social media platforms. Given the absence of existing natural language processing (NLP) tools for Albanian, the system uses a custom-built NLP pipeline, incorporating manual data preprocessing steps such as tokenization, lemmatization, and sentiment analysis. By integrating these preprocessing procedures with machine learning models for trend analysis and opinion classification, the system empowers urban planners to make data-driven decisions based on real-time public feedback. This work demonstrates the potential for additional automation and highlights the adaptability of AI techniques in addressing language-specific resource constraints.I documenti in SFERA sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
