Multidimensional sentiment analysis method on social media data: comparison of emotions during and after the COVID-19 pandemic

dc.contributor.authorDogan, Bulent
dc.contributor.authorBalcioglu, Yavuz Selim
dc.contributor.authorElçi, Meral
dc.date.accessioned2025-10-29T11:16:01Z
dc.date.issued2025
dc.departmentFakülteler, İşletme Fakültesi, İşletme Bölümü
dc.description.abstractPurposeThis study aims to elucidate the dynamics of social media discourse during global health events, specifically investigating how users across different platforms perceive, react to and engage with information concerning such crises.Design/methodology/approachA mixed-method approach was employed, combining both quantitative and qualitative data collection. Initially, thematic analysis was applied to a data set of social media posts across four major platforms over a 12-month period. This was followed by sentiment analysis to discern the predominant emotions embedded within these communications. Statistical tools were used to validate findings, ensuring robustness in the results.FindingsThe results showcased discernible thematic and emotional disparities across platforms. While some platforms leaned toward factual information dissemination, others were rife with user sentiments, anecdotes and personal experiences. Overall, a global sense of concern was evident, but the ways in which this concern manifested varied significantly between platforms.Research limitations/implicationsThe primary limitation is the potential non-representativeness of the sample, as only four major social media platforms were considered. Future studies might expand the scope to include emerging platforms or non-English language platforms. Additionally, the rapidly evolving nature of social media discourse implies that findings might be time-bound, necessitating periodic follow-up studies.Practical implicationsUnderstanding the nature of discourse on various platforms can guide health organizations, policymakers and communicators in tailoring their messages. Recognizing where factual information is required, versus where sentiment and personal stories resonate, can enhance the efficacy of public health communication strategies.Social implicationsThe study underscores the societal reliance on social media for information during crises. Recognizing the different ways in which communities engage with, and are influenced by, platform-specific discourse can help in fostering a more informed and empathetic society, better equipped to handle global challenges.Originality/valueThis research is among the first to offer a comprehensive, cross-platform analysis of social media discourse during a global health event. By comparing user engagement across platforms, it provides unique insights into the multifaceted nature of public sentiment and information dissemination during crises.
dc.identifier.doi10.1108/K-09-2023-1808
dc.identifier.endpage2456
dc.identifier.issn0368-492X
dc.identifier.issn1758-7883
dc.identifier.issue4
dc.identifier.orcid0000-0001-7138-2972
dc.identifier.scopus2-s2.0-105001073932
dc.identifier.scopusqualityQ1
dc.identifier.startpage2414
dc.identifier.urihttps://doi.org/10.1108/K-09-2023-1808
dc.identifier.urihttps://hdl.handle.net/20.500.14854/7377
dc.identifier.volume54
dc.identifier.wosWOS:001137434900001
dc.identifier.wosqualityQ2
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherEmerald Group Publishing Ltd
dc.relation.ispartofKybernetes
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_WOS_20251020
dc.subjectEmotion analysis
dc.subjectSentiment analysis
dc.subjectCOVID-19
dc.subjectContent analysis
dc.subjectNatural language processing
dc.titleMultidimensional sentiment analysis method on social media data: comparison of emotions during and after the COVID-19 pandemic
dc.typeArticle

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