A Comparison of “X” Sentiment Analysis Investigating the Impact of COVID-19 on “Essential Jobs”

Vahdatnia, Ali [Verfasser]; Peachkah, Danoosh [Verfasser]; Tiemann, Michael [Verfasser]

A Comparison of “X” Sentiment Analysis Investigating the Impact of COVID-19 on “Essential Jobs”

bibb.id	784674
bibb.participation	BIBB-Mitarbeiter	de
dc.contributor.author	Vahdatnia, Ali [Verfasser]	de
dc.contributor.author	Peachkah, Danoosh [Verfasser]	de
dc.contributor.author	Tiemann, Michael [Verfasser]	de
dc.date.accessioned	2026-04-07T16:18:36Z
dc.date.available	2026-04-07T16:18:36Z
dc.date.issued	2025
dc.description.abstract	"This paper investigates transformations of 'Essential Jobs' during the COVID-19 pandemic through sentiment analysis of social media data, specifically focusing on 'X' posts. The study employs a comprehensive methodology consisting of traditional and modern sentiment analysis tools as well as advanced deep learning approaches to examine job-related sentiments across English and German languages. The research demonstrates that the 'Twitter-XLM-RoBERTa' model outperforms other sentiment analysis tools in both base and enhanced implementations, challenging the assumption that deep learning enhancements necessarily improve sentiment analysis performance. The findings indicate significant variations between 'Essential Job' designations. However, the high proportion of 'No-Data' classifications and linguistic variability between English and German datasets suggest methodological limitations." (Authors' abstract, BIBB-Doku)	de
dc.description.statementofresponsibility	Ali Vahdatnia, Danoosh Peachkah, Michael Tiemann	de
dc.format.extent	Seite 58-67	de
dc.format.illustration	Illustrationen	de
dc.format.illustration	Diagramme	de
dc.format.medium	Elektronische Ressource	de
dc.format.medium	Sammelbandbeitrag	de
dc.identifier.uri	https://bibb-dspace.bibb.de/jspui/handle/BIBB/784674
dc.language.iso	en	de
dc.rdacarrier.code	cr	de
dc.rdacarrier.source	rdacontent	de
dc.rdacarrier.term	Online-Ressource	de
dc.rdacontent.code	txt	de
dc.rdacontent.source	rdacontent	de
dc.rdacontent.term	Text	de
dc.rdamedia.code	c	de
dc.rdamedia.source	rdacontent	de
dc.rdamedia.term	Computermedien	de
dc.relation.ispartof	Proceedings of the 2nd International Workshop on AI in Society, Education and Educational Research (AISEER 2025) co-located with 28th European Conference on Artificial Intelligence (ECAI 2025), October 25, 2025. Bologna, Italy / Jens Dörpinghaus [Hrsg.] ; Michael Tiemann [Hrsg.] ; Robert Helmrich [Hrsg.]
dc.rights	Namensnennung 4.0 International	*
dc.rights.uri	https://creativecommons.org/licenses/by/4.0/	*
dc.subject.classification	T 4 Bezugswissenschaften	de
dc.subject.ddc	370	de
dc.subject.ddc	300	de
dc.subject.other	Linguistik	de
dc.subject.other	Berufsklassifikation	de
dc.subject.other	Social Media	de
dc.subject.other	Pandemie	de
dc.title	A Comparison of “X” Sentiment Analysis Investigating the Impact of COVID-19 on “Essential Jobs”	de

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Proceedings of the 2nd International Workshop on AI in Society, Education and Educational Research (AISEER 2025) co-located with 28th European Conference on Artificial Intelligence (ECAI 2025), October 25, 2025. Bologna, Italy / Jens Dörpinghaus [Hrsg.] ; Michael Tiemann [Hrsg.] ; Robert Helmrich [Hrsg.]
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