IDEAS home Printed from https://ideas.repec.org/a/spr/qualqt/v57y2023i3d10.1007_s11135-022-01460-3.html
   My bibliography  Save this article

A text data mining approach to the study of emotions triggered by new advertising formats during the COVID-19 pandemic

Author

Listed:
  • Angela Maria D’Uggento

    (University of Bari Aldo Moro)

  • Albino Biafora

    (University of Bari Aldo Moro)

  • Fabio Manca

    (University of Bari Aldo Moro)

  • Claudia Marin

    (University of Bari Aldo Moro)

  • Massimo Bilancia

    (University of Bari Aldo Moro)

Abstract

Under the influence of the health emergency triggered by the COVID-19 pandemic, many brands changed their communication strategy and included more or less explicit references to the principles of solidarity and fraternity in their TV commercials to boost the confidence and hope of Italian families during the lockdown. The traditional attitudes of the advertising format, which focused on product characteristics, were relegated to the background in order to reinforce the “brand image” through words, signs, hashtags and music that spread empathetic messages to all those who needed to regain hope and trust in a time of extreme emotional fragility. The objective of this paper is to identify the emotions and brand awareness during the lockdown using text mining techniques by measuring customer sentiment expressed on the Twitter social network. Our proposal starts from an unstructured corpus of 20,982 tweets processed with text data mining techniques to identify patterns and trends in people’s posts related to specific hashtags and TV ads produced during the COVID-19 pandemic. The innovations in the brand’s advertising among consumers seem to have triggered some sense of appreciation and gratitude, as well as a strong sense of belonging that was not present before, as the TV ads were perceived as a disruptive element in consumers’ tweets. Although this effect is clearly documented, in this paper we demonstrate its transitory nature, in the sense that the frequency of occurrence of terms associated with an emotional dimension peaks during the weeks of lockdown, and then gradually decreases.

Suggested Citation

  • Angela Maria D’Uggento & Albino Biafora & Fabio Manca & Claudia Marin & Massimo Bilancia, 2023. "A text data mining approach to the study of emotions triggered by new advertising formats during the COVID-19 pandemic," Quality & Quantity: International Journal of Methodology, Springer, vol. 57(3), pages 2303-2325, June.
    • Handle: RePEc:spr:qualqt:v:57:y:2023:i:3:d:10.1007_s11135-022-01460-3
    DOI: 10.1007/s11135-022-01460-3
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11135-022-01460-3
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s11135-022-01460-3?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
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. David M. Blei & Alp Kucukelbir & Jon D. McAuliffe, 2017. "Variational Inference: A Review for Statisticians," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 112(518), pages 859-877, April.
    2. Leonora Fuxman & I. Hilmi Elifoglu & Chiang-nan Chao & Tiger Li, 2014. "Digital Advertising: A More Effective Way to Promote Businesses¡¯ Products," Journal of Business Administration Research, Journal of Business Administration Research, Sciedu Press, vol. 3(2), pages 59-67, October.
    3. Calder, Bobby J. & Malthouse, Edward C. & Schaedel, Ute, 2009. "An Experimental Study of the Relationship between Online Engagement and Advertising Effectiveness," Journal of Interactive Marketing, Elsevier, vol. 23(4), pages 321-331.
    4. Young-Joo Lee & Ji-Young Park, 2018. "Identification of future signal based on the quantitative and qualitative text mining: a case study on ethical issues in artificial intelligence," Quality & Quantity: International Journal of Methodology, Springer, vol. 52(2), pages 653-667, March.
    5. 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.
    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. Shen Liu & Hongyan Liu, 2021. "Tagging Items Automatically Based on Both Content Information and Browsing Behaviors," INFORMS Journal on Computing, INFORMS, vol. 33(3), pages 882-897, July.
    2. Loaiza-Maya, Rubén & Smith, Michael Stanley & Nott, David J. & Danaher, Peter J., 2022. "Fast and accurate variational inference for models with many latent variables," Journal of Econometrics, Elsevier, vol. 230(2), pages 339-362.
    3. Eigenraam, Anniek W. & Eelen, Jiska & Verlegh, Peeter W.J., 2021. "Let Me Entertain You? The Importance of Authenticity in Online Customer Engagement," Journal of Interactive Marketing, Elsevier, vol. 54(C), pages 53-68.
    4. Wang, Tien & Lee, Fu-Yu, 2020. "Examining customer engagement and brand intimacy in social media context," Journal of Retailing and Consumer Services, Elsevier, vol. 54(C).
    5. Xing Qin & Shuangge Ma & Mengyun Wu, 2023. "Two‐level Bayesian interaction analysis for survival data incorporating pathway information," Biometrics, The International Biometric Society, vol. 79(3), pages 1761-1774, September.
    6. Youngseon Lee & Seongil Jo & Jaeyong Lee, 2022. "A variational inference for the Lévy adaptive regression with multiple kernels," Computational Statistics, Springer, vol. 37(5), pages 2493-2515, November.
    7. Liu, Jie & Ye, Zifeng & Chen, Kun & Zhang, Panpan, 2024. "Variational Bayesian inference for bipartite mixed-membership stochastic block model with applications to collaborative filtering," Computational Statistics & Data Analysis, Elsevier, vol. 189(C).
    8. Nathaniel Tomasetti & Catherine Forbes & Anastasios Panagiotelis, 2019. "Updating Variational Bayes: Fast Sequential Posterior Inference," Monash Econometrics and Business Statistics Working Papers 13/19, Monash University, Department of Econometrics and Business Statistics.
    9. Dessart, Laurence & Pitardi, Valentina, 2019. "How stories generate consumer engagement: An exploratory study," Journal of Business Research, Elsevier, vol. 104(C), pages 183-195.
    10. Gael M. Martin & David T. Frazier & Christian P. Robert, 2020. "Computing Bayes: Bayesian Computation from 1763 to the 21st Century," Monash Econometrics and Business Statistics Working Papers 14/20, Monash University, Department of Econometrics and Business Statistics.
    11. Djohan Bonnet & Tifenn Hirtzlin & Atreya Majumdar & Thomas Dalgaty & Eduardo Esmanhotto & Valentina Meli & Niccolo Castellani & Simon Martin & Jean-François Nodin & Guillaume Bourgeois & Jean-Michel P, 2023. "Bringing uncertainty quantification to the extreme-edge with memristor-based Bayesian neural networks," Nature Communications, Nature, vol. 14(1), pages 1-13, December.
    12. Ho, Paul, 2023. "Global robust Bayesian analysis in large models," Journal of Econometrics, Elsevier, vol. 235(2), pages 608-642.
    13. Liang, Xinbin & Liu, Zhuoxuan & Wang, Jie & Jin, Xinqiao & Du, Zhimin, 2023. "Uncertainty quantification-based robust deep learning for building energy systems considering distribution shift problem," Applied Energy, Elsevier, vol. 337(C).
    14. Raed S. Algharabat & Nripendra P. Rana, 0. "Social Commerce in Emerging Markets and its Impact on Online Community Engagement," Information Systems Frontiers, Springer, vol. 0, pages 1-22.
    15. Kim, Jooyoung & Ahn, Sun Joo (Grace) & Kwon, Eun Sook & Reid, Leonard N., 2017. "TV advertising engagement as a state of immersion and presence," Journal of Business Research, Elsevier, vol. 76(C), pages 67-76.
    16. Gael M. Martin & David T. Frazier & Ruben Loaiza-Maya & Florian Huber & Gary Koop & John Maheu & Didier Nibbering & Anastasios Panagiotelis, 2023. "Bayesian Forecasting in the 21st Century: A Modern Review," Monash Econometrics and Business Statistics Working Papers 1/23, Monash University, Department of Econometrics and Business Statistics.
    17. Waqas, Muhammad & Salleh, Noor Akma Mohd & Hamzah, Zalfa Laili, 2021. "Branded Content Experience in Social Media: Conceptualization, Scale Development, and Validation," Journal of Interactive Marketing, Elsevier, vol. 56(C), pages 106-120.
    18. Seokhyun Chung & Raed Al Kontar & Zhenke Wu, 2022. "Weakly Supervised Multi-output Regression via Correlated Gaussian Processes," INFORMS Joural on Data Science, INFORMS, vol. 1(2), pages 115-137, October.
    19. Gary Koop & Dimitris Korobilis, 2023. "Bayesian Dynamic Variable Selection In High Dimensions," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 64(3), pages 1047-1074, August.
    20. Ziqi Zhang & Xinye Zhao & Mehak Bindra & Peng Qiu & Xiuwei Zhang, 2024. "scDisInFact: disentangled learning for integration and prediction of multi-batch multi-condition single-cell RNA-sequencing data," Nature Communications, Nature, vol. 15(1), pages 1-16, December.

    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:spr:qualqt:v:57:y:2023:i:3:d:10.1007_s11135-022-01460-3. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.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.