Investigation of the Gender-Specific Discourse about Online Learning during COVID-19 on Twitter Using Sentiment Analysis, Subjectivity Analysis, and Toxicity Analysis

Investigation of the Gender-Specific Discourse about Online Learning during COVID-19 on Twitter Using Sentiment Analysis, Subjectivity Analysis, and Toxicity Analysis

This paper presents several novel findings from a comprehensive analysis of about 50,000 Tweets about online learning during COVID-19, posted on Twitter between 9 November 2021 and 13 July 2022. First, the results of sentiment analysis from VADER, Afinn, and TextBlob show that a higher percentage of...

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Bibliographic Details
Main Authors: Nirmalya Thakur, Shuqi Cui, Karam Khanna, Victoria Knieling, Yuvraj Nihal Duggal, Mingchen Shao
Format: Article
Language:English
Published: MDPI AG 2023-10-01
Series:Computers
Subjects:
COVID-19
online learning
Twitter
data analysis
natural language processing
sentiment analysis
Online Access:https://www.mdpi.com/2073-431X/12/11/221

Internet

https://www.mdpi.com/2073-431X/12/11/221

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