People's Emotions Analysis while Watching YouTube Videos

Autor(en): Motyka, Volodymyr
Stepaniak, Yaroslav
Nasalska, Mariia
Vysotska, Victoria
Herausgeber: Cherednichenko, O.
Universite Lumiere Lyon 2
UR ERIC � 5 avenue Mendes France
Bron Cedex
Chyrun, L.
Ivan Franko National University of Lviv
University Street
1
Lviv
Vysotska, V.
Lviv Polytechnic National University and, S. Bandera Street
12
Osnabruck University
Friedrich-Janssen-Str. 1
Osnabruck
Stichwörter: Cluster analysis; correlation; Correlation methods; Data visualization; dislike; emotion; Emotion analysis; like; Number of views; sentiment analysis; smoothing; Smoothing methods; YouTube
Erscheinungsdatum: 2023
Herausgeber: CEUR-WS
Journal: CEUR Workshop Proceedings
Volumen: 3403
Startseite: 500 – 525
Zusammenfassung: 
For analysis, a dataset containing information about videos from video hosting YouTube is selected, namely: title, video category, channel (author), number of views, number of likes, number of dislikes, date of video release. The purpose of the study was to analyze the state of people while watching videos on this platform. For this, various methods of visualization and data processing, smoothing methods, correlation and cluster analysis are used. © 2023 Copyright for this paper by its authors.
Beschreibung: 
Cited by: 0; Conference name: 7th International Conference on Computational Linguistics and Intelligent Systems. Volume III: Intelligent Systems Workshop, CoLInS 2023; Conference date: 20 April 2023 through 21 April 2023; Conference code: 188984
ISSN: 1613-0073
Externe URL: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85163088367&partnerID=40&md5=ead1545ba3875de17ba4402fbdcdf3b0

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