IDEAS home Printed from https://ideas.repec.org/a/cog/urbpla/v3y2018i1p21-33.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.cogitatiopress.com/urbanplanning/article/view/1231
    Download Restriction: no
    File URL: https://libkey.io/10.17645/up.v3i1.1231?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Anna Kovacs-Gyori & Alina Ristea & Clemens Havas & Bernd Resch & Pablo Cabrera-Barona, 2018. "#London2012: Towards Citizen-Contributed Urban Planning Through Sentiment Analysis of Twitter Data," Urban Planning, Cogitatio Press, vol. 3(1), pages 75-99.
    2. 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.
    3. Irina Wedel & Michael Palk & Stefan Voß, 2022. "A Bilingual Comparison of Sentiment and Topics for a Product Event on Twitter," Information Systems Frontiers, Springer, vol. 24(5), pages 1635-1646, October.
    4. Smith, Andrew N. & Fischer, Eileen & Yongjian, Chen, 2012. "How Does Brand-related User-generated Content Differ across YouTube, Facebook, and Twitter?," Journal of Interactive Marketing, Elsevier, vol. 26(2), pages 102-113.
    5. Xuan Yang & Xiao Li & Daning Hu & Harry Jiannan Wang, 2021. "Differential impacts of social influence on initial and sustained participation in open source software projects," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 72(9), pages 1133-1147, September.
    6. Pilvi Nummi, 2018. "Crowdsourcing Local Knowledge with PPGIS and Social Media for Urban Planning to Reveal Intangible Cultural Heritage," Urban Planning, Cogitatio Press, vol. 3(1), pages 100-115.
    7. Bertrand Jayles & Clément Sire & Ralf H J M Kurvers, 2021. "Crowd control: Reducing individual estimation bias by sharing biased social information," PLOS Computational Biology, Public Library of Science, vol. 17(11), pages 1-28, November.
    8. Jalees, Tariq & Tariq, Huma & Zaman, Syed Imran & Alam Kazmi, Syed Hasnain, 2015. "Social Media in Virtual Marketing," MPRA Paper 69868, University Library of Munich, Germany, revised 10 Apr 2015.
    9. Langley, David J. & Hoeve, Maarten C. & Ortt, J. Roland & Pals, Nico & van der Vecht, Bob, 2014. "Patterns of Herding and their Occurrence in an Online Setting," Journal of Interactive Marketing, Elsevier, vol. 28(1), pages 16-25.
    10. Yong Gao & Yuanyuan Chen & Lan Mu & Shize Gong & Pengcheng Zhang & Yu Liu, 2022. "Measuring urban sentiments from social media data: a dual-polarity metric approach," Journal of Geographical Systems, Springer, vol. 24(2), pages 199-221, April.
    11. Ines Küster & Asuncion Hernández, 2012. "Brand impact on purchase intention. An approach in social networks channel," Economics and Business Letters, Oviedo University Press, vol. 1(2), pages 1-9.
    12. Aleksandar Bradic, 2012. "The Role of Social Feedback in Financing of Technology Ventures," Papers 1301.2196, arXiv.org.
    13. Lashgari, Maryam, 2014. "Social Media Technology Deployment in B2B: A Case Study," INDEK Working Paper Series 2014/9, Royal Institute of Technology, Department of Industrial Economics and Management.
    14. Xuzhen Zhu & Jinming Ma & Xin Su & Hui Tian & Wei Wang & Shimin Cai, 2019. "Information Spreading on Weighted Multiplex Social Network," Complexity, Hindawi, vol. 2019, pages 1-15, November.
    15. Li, Xin & Xie, Qianqian & Jiang, Jiaojiao & Zhou, Yuan & Huang, Lucheng, 2019. "Identifying and monitoring the development trends of emerging technologies using patent analysis and Twitter data mining: The case of perovskite solar cell technology," Technological Forecasting and Social Change, Elsevier, vol. 146(C), pages 687-705.
    16. Moro, Sérgio & Rita, Paulo & Vala, Bernardo, 2016. "Predicting social media performance metrics and evaluation of the impact on brand building: A data mining approach," Journal of Business Research, Elsevier, vol. 69(9), pages 3341-3351.
    17. Ruixue Liu & Jing Xiao, 2020. "Factors Affecting Users’ Satisfaction with Urban Parks through Online Comments Data: Evidence from Shenzhen, China," IJERPH, MDPI, vol. 18(1), pages 1-22, December.
    18. Kim Holmberg & Mike Thelwall, 2014. "Disciplinary differences in Twitter scholarly communication," Scientometrics, Springer;Akadémiai Kiadó, vol. 101(2), pages 1027-1042, November.
    19. Shu-Hsun Ho & Yu-Ling Lin & Robert Carlson Patrick, 2015. "Participant Motivations In A Social Media Community Page," Global Journal of Business Research, The Institute for Business and Finance Research, vol. 9(4), pages 67-75.
    20. Mohammed Abdul-Rahman & Wale Alade & Shahnawaz Anwer, 2023. "A Composite Resilience Index (CRI) for Developing Resilience and Sustainability in University Towns," Sustainability, MDPI, vol. 15(4), pages 1-27, February.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:cog:urbpla:v3:y:2018:i:1:p:21-33. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: António Vieira or IT Department (email available below). General contact details of provider: https://www.cogitatiopress.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.