#London2012: Towards Citizen-Contributed Urban Planning Through Sentiment Analysis of Twitter Data

Anna Kovacs-Gyori, Alina Ristea, Clemens Havas, Bernd Resch and Pablo Cabrera-Barona
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Anna Kovacs-Gyori: Department of Geoinformatics—Z_GIS, University of Salzburg, Austria
Alina Ristea: Department of Geoinformatics—Z_GIS, University of Salzburg, Austria
Clemens Havas: Department of Geoinformatics—Z_GIS, University of Salzburg, Austria
Bernd Resch: Department of Geoinformatics—Z_GIS, University of Salzburg, Austria / Center for Geographic Analysis, Harvard University, USA
Pablo Cabrera-Barona: Institute of Higher National Studies—IAEN, Ecuador / Latin American Social Sciences Institute—FLACSO, Ecuador

Urban Planning, 2018, vol. 3, issue 1, 75-99

Abstract: The dynamic nature of cities, understood as complex systems with a variety of concurring factors, poses significant challenges to urban analysis for supporting planning processes. This particularly applies to large urban events because their characteristics often contradict daily planning routines. Due to the availability of large amounts of data, social media offer the possibility for fine-scale spatial and temporal analysis in this context, especially regarding public emotions related to varied topics. Thus, this article proposes a combined approach for analyzing large sports events considering event days vs comparison days (before or after the event) and different user groups (residents vs visitors), as well as integrating sentiment analysis and topic extraction. Our results based on various analyses of tweets demonstrate that different spatial and temporal patterns can be identified, clearly distinguishing both residents and visitors, along with positive or negative sentiment. Furthermore, we could assign tweets to specific urban events or extract topics related to the transportation infrastructure. Although the results are potentially able to support urban planning processes of large events, the approach still shows some limitations including well-known biases in social media or shortcomings in identifying the user groups and in the topic modeling approach.

Keywords: geographic information; GIS; Olympic Games; planned events; sentiment analysis; social media; spatiotemporal analysis; topic extraction (search for similar items in EconPapers)
Date: 2018
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Persistent link: https://EconPapers.repec.org/RePEc:cog:urbpla:v3:y:2018:i:1:p:75-99

DOI: 10.17645/up.v3i1.1287

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