Machine Learning Techniques for Sentiment Analysis of COVID-19-Related Twitter Data

DOI zum Zitieren der Version auf EPub Bayreuth: https://doi.org/10.15495/EPub_UBT_00007580
URN to cite this document: urn:nbn:de:bvb:703-epub-7580-8

Title data

Braig, Niklas ; Benz, Alina ; Voth, Soeren ; Breitenbach, Johannes ; Büttner, Ricardo:
Machine Learning Techniques for Sentiment Analysis of COVID-19-Related Twitter Data.
In: IEEE Access. Vol. 11 (2023) . - pp. 14778-14803.
ISSN 2169-3536
DOI der Verlagsversion: https://doi.org/10.1109/ACCESS.2023.3242234

[thumbnail of Machine_Learning_Techniques_for_Sentiment_Analysis_of_COVID-19-Related_Twitter_Data.pdf]

Format:	PDF
Name:	Machine_Learning_Techniques_for_Sentiment_Analysis_of_COVID-19-Related_Twitter_Data.pdf
Version:	Published Version
Available under License	Creative Commons BY 4.0: Attribution

Download (5MB)

Project information

Project title:	Project's official title Project's id Open Access Publizieren No information

Further data

Item Type:	Article in a journal
Keywords:	Behavioral science; COVID-19; deep learning; machine learning; sentiment analysis; social science; twitter
DDC Subjects:	000 Computer Science, information, general works > 004 Computer science 300 Social sciences > 330 Economics
Institutions of the University:	Faculties > Faculty of Law, Business and Economics > Department of Business Administration > Chair Business Administration XVIII - Information Systems Management and Data Science > Chair Business Administration XVIII - Information Systems Management and Data Science - Univ.-Prof. Dr. Ricardo Büttner Faculties Faculties > Faculty of Law, Business and Economics Faculties > Faculty of Law, Business and Economics > Department of Business Administration Faculties > Faculty of Law, Business and Economics > Department of Business Administration > Chair Business Administration XVIII - Information Systems Management and Data Science
Language:	English
Originates at UBT:	Yes
URN:	urn:nbn:de:bvb:703-epub-7580-8
Date Deposited:	18 Mar 2024 10:09
Last Modified:	18 Mar 2024 10:11
URI:	https://epub.uni-bayreuth.de/id/eprint/7580

Download Statistics

Downloads

Downloads per month over past year