IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v15y2023i22p16106-d1283530.html
   My bibliography  Save this article

‘I Tweet about Our #GreenEnergy’—Automated Classification of Social Identity and Opinion Mining of the Dutch Twitter Discourse on Green-Energy Technologies

Author

Listed:
  • Romée Lammers

    (Faculty of Behavioral, Management and Social Sciences, University of Twente, Drienerlolaan 5, 7522 NB Enschede, The Netherlands)

  • Sikke R. Jansma

    (Faculty of Behavioral, Management and Social Sciences, University of Twente, Drienerlolaan 5, 7522 NB Enschede, The Netherlands)

  • Bernard P. Veldkamp

    (Faculty of Behavioral, Management and Social Sciences, University of Twente, Drienerlolaan 5, 7522 NB Enschede, The Netherlands)

  • Anna K. Machens

    (Faculty of Behavioral, Management and Social Sciences, University of Twente, Drienerlolaan 5, 7522 NB Enschede, The Netherlands)

  • Matthias de Visser

    (Faculty of Behavioral, Management and Social Sciences, University of Twente, Drienerlolaan 5, 7522 NB Enschede, The Netherlands)

  • Jordy F. Gosselt

    (Faculty of Behavioral, Management and Social Sciences, University of Twente, Drienerlolaan 5, 7522 NB Enschede, The Netherlands)

Abstract

Understanding the complexities of public opinion is crucial for a green-energy transition. This present study examines the sentiment of public opinion towards various energy technologies on Twitter during the Dutch 2021 general elections. A dataset comprising 186,822 tweets and profile descriptions was analyzed using two automated text classifiers to explore how individuals with different self-proclaimed identities perceive green-energy technologies. The analysis involved the application of the sentiment and social identity classifier models, followed by a frequency and co-occurrence analysis. The findings revealed a negative overall sentiment towards green-energy technologies in the Twitter discourse. It further showed that perceptions may differ depending on a technology’s development stage, with emerging technologies generally receiving more favorable views compared to established ones. Furthermore, it was found that, although there is a general trend of negative sentiment based on political identity, and positive sentiment based on occupational identity, this trend did not consistently apply to specific energy technologies. This discrepancy can likely be attributed to varying implementation effects and contextual situations associated with the technologies. The findings suggest that personalized communication strategies for specific social groups may be beneficial for understanding and addressing public opinions, needs, and concerns within the energy transition. The complexity of understanding public opinion in the context of green-energy highlights the need for a nuanced approach in future research.

Suggested Citation

  • Romée Lammers & Sikke R. Jansma & Bernard P. Veldkamp & Anna K. Machens & Matthias de Visser & Jordy F. Gosselt, 2023. "‘I Tweet about Our #GreenEnergy’—Automated Classification of Social Identity and Opinion Mining of the Dutch Twitter Discourse on Green-Energy Technologies," Sustainability, MDPI, vol. 15(22), pages 1-23, November.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:22:p:16106-:d:1283530
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/15/22/16106/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/15/22/16106/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Jochen Markard, 2018. "The next phase of the energy transition and its implications for research and policy," Nature Energy, Nature, vol. 3(8), pages 628-633, August.
    2. Feinerer, Ingo & Hornik, Kurt & Meyer, David, 2008. "Text Mining Infrastructure in R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 25(i05).
    3. Xia, Dongqin & Li, Yazhou & He, Yanling & Zhang, Tingting & Wang, Yongliang & Gu, Jibao, 2019. "Exploring the role of cultural individualism and collectivism on public acceptance of nuclear energy," Energy Policy, Elsevier, vol. 132(C), pages 208-215.
    4. Serena Y. Kim & Koushik Ganesan & Princess Dickens & Soumya Panda, 2021. "Public Sentiment toward Solar Energy—Opinion Mining of Twitter Using a Transformer-Based Language Model," Sustainability, MDPI, vol. 13(5), pages 1-19, March.
    5. Zhang, Shengling & Li, Yue & Hao, Yu & Zhang, Yipeng, 2018. "Does public opinion affect air quality? Evidence based on the monthly data of 109 prefecture-level cities in China," Energy Policy, Elsevier, vol. 116(C), pages 299-311.
    6. Joeri Rogelj & Michel den Elzen & Niklas Höhne & Taryn Fransen & Hanna Fekete & Harald Winkler & Roberto Schaeffer & Fu Sha & Keywan Riahi & Malte Meinshausen, 2016. "Paris Agreement climate proposals need a boost to keep warming well below 2 °C," Nature, Nature, vol. 534(7609), pages 631-639, June.
    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. Wang, Bingzheng & Lu, Xiaofei & Zhang, Cancan & Wang, Hongsheng, 2022. "Cascade and hybrid processes for co-generating solar-based fuels and electricity via combining spectral splitting technology and membrane reactor," Renewable Energy, Elsevier, vol. 196(C), pages 782-799.
    2. Li, Qiang & Wang, Shengying & He, Zichun & Li, Hanqiao & Xiang, Erwei, 2023. "Does stock market index adjustment affect environmental information disclosure? Evidence from China," International Review of Financial Analysis, Elsevier, vol. 87(C).
    3. Sapkota, Krishna & Gemechu, Eskinder & Oni, Abayomi Olufemi & Ma, Linwei & Kumar, Amit, 2022. "Greenhouse gas emissions from Canadian oil sands supply chains to China," Energy, Elsevier, vol. 251(C).
    4. Piris-Cabezas, Pedro & Lubowski, Ruben N. & Leslie, Gabriela, 2023. "Estimating the potential of international carbon markets to increase global climate ambition," World Development, Elsevier, vol. 167(C).
    5. Grinis, Inna, 2017. "The STEM requirements of "non-STEM" jobs: evidence from UK online vacancy postings and implications for skills & knowledge shortages," LSE Research Online Documents on Economics 85123, London School of Economics and Political Science, LSE Library.
    6. Violeta Mihaela Dincă & Mihail Busu & Zoltan Nagy-Bege, 2022. "Determinants with Impact on Romanian Consumers’ Energy-Saving Habits," Energies, MDPI, vol. 15(11), pages 1-18, June.
    7. Julia Bachtrögler & Christoph Hammer & Wolf Heinrich Reuter & Florian Schwendinger, 2019. "Guide to the galaxy of EU regional funds recipients: evidence from new data," Empirica, Springer;Austrian Institute for Economic Research;Austrian Economic Association, vol. 46(1), pages 103-150, February.
    8. Alt, Marius & Gallier, Carlo & Kesternich, Martin & Sturm, Bodo, 2023. "Collective minimum contributions to counteract the ratchet effect in the voluntary provision of public goods," Journal of Environmental Economics and Management, Elsevier, vol. 122(C).
    9. Rong Li & Brent Sohngen & Xiaohui Tian, 2022. "Efficiency of forest carbon policies at intensive and extensive margins," American Journal of Agricultural Economics, John Wiley & Sons, vol. 104(4), pages 1243-1267, August.
    10. Róbert Csalódi & Tímea Czvetkó & Viktor Sebestyén & János Abonyi, 2022. "Sectoral Analysis of Energy Transition Paths and Greenhouse Gas Emissions," Energies, MDPI, vol. 15(21), pages 1-26, October.
    11. Dehler-Holland, Joris & Schumacher, Kira & Fichtner, Wolf, 2021. "Topic Modeling Uncovers Shifts in Media Framing of the German Renewable Energy Act," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, vol. 2(1).
    12. Sanzana Tabassum & Tanvin Rahman & Ashraf Ul Islam & Sumayya Rahman & Debopriya Roy Dipta & Shidhartho Roy & Naeem Mohammad & Nafiu Nawar & Eklas Hossain, 2021. "Solar Energy in the United States: Development, Challenges and Future Prospects," Energies, MDPI, vol. 14(23), pages 1-65, December.
    13. Shuyue Huang & Lena Jingen Liang & Hwansuk Chris Choi, 2022. "How We Failed in Context: A Text-Mining Approach to Understanding Hotel Service Failures," Sustainability, MDPI, vol. 14(5), pages 1-18, February.
    14. Laura Anderlucci & Cinzia Viroli, 2020. "Mixtures of Dirichlet-Multinomial distributions for supervised and unsupervised classification of short text data," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 14(4), pages 759-770, December.
    15. Qi, Wen-Hui & Qi, Ming-Liang & Ji, Ya-Min, 2020. "The effect path of public communication on public acceptance of nuclear energy," Energy Policy, Elsevier, vol. 144(C).
    16. Radtke, Jörg & Scherhaufer, Patrick, 2022. "A social science perspective on conflicts in the energy transition: An introduction to the special issue," Utilities Policy, Elsevier, vol. 78(C).
    17. Heleen L. Soest & Lara Aleluia Reis & Luiz Bernardo Baptista & Christoph Bertram & Jacques Després & Laurent Drouet & Michel Elzen & Panagiotis Fragkos & Oliver Fricko & Shinichiro Fujimori & Neil Gra, 2021. "Global roll-out of comprehensive policy measures may aid in bridging emissions gap," Nature Communications, Nature, vol. 12(1), pages 1-10, December.
    18. Thananya Janhuaton & Vatanavongs Ratanavaraha & Sajjakaj Jomnonkwao, 2024. "Forecasting Thailand’s Transportation CO 2 Emissions: A Comparison among Artificial Intelligent Models," Forecasting, MDPI, vol. 6(2), pages 1-23, June.
    19. Li, Aitong & Xu, Yuan & Shiroyama, Hideaki, 2019. "Solar lobby and energy transition in Japan," Energy Policy, Elsevier, vol. 134(C).
    20. Stefano Sbalchiero & Maciej Eder, 2020. "Topic modeling, long texts and the best number of topics. Some Problems and solutions," Quality & Quantity: International Journal of Methodology, Springer, vol. 54(4), pages 1095-1108, August.

    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:gam:jsusta:v:15:y:2023:i:22:p:16106-:d:1283530. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.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.