Machine Learning Approaches for Classifying the Distribution of Covid-19 Sentiments

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ISSN Print: 2161-718X
ISSN Online: 2161-7198
www.scirp.org/journal/ojs
E-mail: ojs@scirp.org

"Machine Learning Approaches for Classifying the Distribution of Covid-19 Sentiments"
written by M. Kuyo, S. Mwalili, E. Okang’o,
published by Open Journal of Statistics, Vol.11 No.5, 2021

has been cited by the following article(s):

[1]	Machine Learning Approaches to Assess Mood of the News Editorial
	2022 IEEE International …, 2022

[2]	Public Sentiment Assessment of Coronavirus-Specific Tweets using a Transformer-based BERT Classifier
	2022 International Conference on …, 2022

[3]	Exploring COVID-19 public perceptions in South Africa through sentiment analysis and topic modelling of Twitter posts
	The African Journal of Information …, 2022

[4]	Predicting COVID-19 community infection relative risk with a Dynamic Bayesian Network
	Frontiers in Public Health, 2022

[1]	Proceedings of the 2nd International Conference on Cognitive Based Information Processing and Applications (CIPA 2022) Lecture Notes on Data Engineering and Communications Technologies, 2023 DOI:10.1007/978-981-19-9376-3_88

[2]	Machine Learning Approaches to Assess Mood of the News Editorial 2022 IEEE International Conference on Electronics, Computing and Communication Technologies (CONECCT), 2022 DOI:10.1109/CONECCT55679.2022.9865842

[3]	Predicting COVID-19 community infection relative risk with a Dynamic Bayesian Network Frontiers in Public Health, 2022 DOI:10.3389/fpubh.2022.876691

[4]	Public Sentiment Assessment of Coronavirus-Specific Tweets using a Transformer-based BERT Classifier 2022 International Conference on Edge Computing and Applications (ICECAA), 2022 DOI:10.1109/ICECAA55415.2022.9936448

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