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Investigating the Emotional Responses of Individuals to Urban Green Space Using Twitter Data: A Critical Comparison of Three Different Methods of Sentiment Analysis

Author

Listed:
  • Helen Roberts

    (School of Geography, Earth and Environmental Sciences, University of Birmingham, UK)

  • Bernd Resch

    (Department of Geoinformatics, University of Salzburg, Austria / Center for Geographic Analysis, Harvard University, USA)

  • Jon Sadler

    (School of Geography, Earth and Environmental Sciences, University of Birmingham, UK)

  • Lee Chapman

    (School of Geography, Earth and Environmental Sciences, University of Birmingham, UK)

  • Andreas Petutschnig

    (Department of Geoinformatics, University of Salzburg, Austria)

  • Stefan Zimmer

    (Department of Geoinformatics, University of Salzburg, Austria)

Abstract

In urban research, Twitter data have the potential to provide additional information about urban citizens, their activities, mobility patterns and emotion. Extracting the sentiment present in tweets is increasingly recognised as a valuable approach to gathering information on the mood, opinion and emotional responses of individuals in a variety of contexts. This article evaluates the potential of deriving emotional responses of individuals while they experience and interact with urban green space. A corpus of over 10,000 tweets relating to 60 urban green spaces in Birmingham, United Kingdom was analysed for positivity, negativity and specific emotions, using manual, semi-automated and automated methods of sentiment analysis and the outputs of each method compared. Similar numbers of tweets were annotated as positive/neutral/negative by all three methods; however, inter-method consistency in tweet assignment between the methods was low. A comparison of all three methods on the same corpus of tweets, using character emojis as an additional quality control, identifies a number of limitations associated with each approach. The results presented have implications for urban planners in terms of the choices available to identify and analyse the sentiment present in tweets, and the importance of choosing the most appropriate method. Future attempts to develop more reliable and accurate algorithms of sentiment analysis are needed and should focus on semi-automated methods.

Suggested Citation

  • Helen Roberts & Bernd Resch & Jon Sadler & Lee Chapman & Andreas Petutschnig & Stefan Zimmer, 2018. "Investigating the Emotional Responses of Individuals to Urban Green Space Using Twitter Data: A Critical Comparison of Three Different Methods of Sentiment Analysis," Urban Planning, Cogitatio Press, vol. 3(1), pages 21-33.
    • Handle: RePEc:cog:urbpla:v3:y:2018:i:1:p:21-33
    DOI: 10.17645/up.v3i1.1231
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    References listed on IDEAS

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    1. Bernard J. Jansen & Mimi Zhang & Kate Sobel & Abdur Chowdury, 2009. "Twitter power: Tweets as electronic word of mouth," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 60(11), pages 2169-2188, November.
    2. Bernd Resch & Anja Summa & Peter Zeile & Michael Strube, 2016. "Citizen-Centric Urban Planning through Extracting Emotion Information from Twitter in an Interdisciplinary Space-Time-Linguistics Algorithm," Urban Planning, Cogitatio Press, vol. 1(2), pages 114-127.
    3. Bernd Resch & Anja Summa & Peter Zeile & Michael Strube, 2016. "Citizen-Centric Urban Planning through Extracting Emotion Information from Twitter in an Interdisciplinary Space-Time-Linguistics Algorithm," Urban Planning, Cogitatio Press, vol. 1(2), pages 114-127.
    4. Marc Cheong & Vincent C. S. Lee, 2011. "A microblogging-based approach to terrorism informatics: Exploration and chronicling civilian sentiment and response to terrorism events via Twitter," Information Systems Frontiers, Springer, vol. 13(1), pages 45-59, March.
    5. Lewis Mitchell & Morgan R Frank & Kameron Decker Harris & Peter Sheridan Dodds & Christopher M Danforth, 2013. "The Geography of Happiness: Connecting Twitter Sentiment and Expression, Demographics, and Objective Characteristics of Place," PLOS ONE, Public Library of Science, vol. 8(5), pages 1-15, May.
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