Citizen-Centric Urban Planning through Extracting Emotion Information from Twitter in an Interdisciplinary Space-Time-Linguistics Algorithm

Open Access Journal | ISSN: 2183-7635

Article | Open Access

Citizen-Centric Urban Planning through Extracting Emotion Information from Twitter in an Interdisciplinary Space-Time-Linguistics Algorithm

  • Bernd Resch Department of Geoinformatics - Z_GIS, University of Salzburg, Austria; Center for Geographic Analysis, Harvard University, Cambridge, USA and Institute of Geography (GIScience), Heidelberg University, Heidelberg, Germany
  • Anja Summa Department of Computational Linguistics, Heidelberg University, Heidelberg, Germany
  • Peter Zeile Computergestützte Planungs und Entwurfsmethoden (CPE), University of Kaiserslautern, Kaiserslautern, Germany
  • Michael Strube NLP Group, Heidelberg Institute for Theoretical Studies gGmbH, Heidelberg, Germany

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Abstract:  Traditional urban planning processes typically happen in offices and behind desks. Modern types of civic participation can enhance those processes by acquiring citizens’ ideas and feedback in participatory sensing approaches like “People as Sensors”. As such, citizen-centric planning can be achieved by analysing Volunteered Geographic Information (VGI) data such as Twitter tweets and posts from other social media channels. These user-generated data comprise several information dimensions, such as spatial and temporal information, and textual content. However, in previous research, these dimensions were generally examined separately in single-disciplinary approaches, which does not allow for holistic conclusions in urban planning. This paper introduces TwEmLab, an interdisciplinary approach towards extracting citizens’ emotions in different locations within a city. More concretely, we analyse tweets in three dimensions (space, time, and linguistics), based on similarities between each pair of tweets as defined by a specific set of functional relationships in each dimension. We use a graph-based semi-supervised learning algorithm to classify the data into discrete emotions (happiness, sadness, fear, anger/disgust, none). Our proposed solution allows tweets to be classified into emotion classes in a multi-parametric approach. Additionally, we created a manually annotated gold standard that can be used to evaluate TwEmLab’s performance. Our experimental results show that we are able to identify tweets carrying emotions and that our approach bears extensive potential to reveal new insights into citizens’ perceptions of the city.

Keywords:  integrated space-time-linguistics methodology; participatory planning; semi-supervised learning; Twitter emotions

Published:   5 July 2016


© Bernd Resch, Anja Summa, Peter Zeile, Michael Strube. This is an open access article distributed under the terms of the Creative Commons Attribution 4.0 license (, which permits any use, distribution, and reproduction of the work without further permission provided the original author(s) and source are credited.