Utilizing Sentiment Analysis to Enhance the Quality of Online Learning
5th National Conference of Saudi Computers Colleges, NCCC 2022
; : 41-46, 2022.
Article
in English
| Scopus | ID: covidwho-2291095
ABSTRACT
The COVID-19 pandemic spread worldwide in the year 2020 and became a global health emergency. This pandemic has brought awareness that social distancing and quarantine are ideal ways to protect people in the community from infection. Therefore, Saudi Arabia used online learning instead of stopping it completely to continue the education process. This paper proposes to use machine-learning algorithms for Arabic sentiment analysis to find out what students and teaching staff thought about online learning during the COVID-19 outbreak. During the pandemic, a real-world data set was gathered that included about 100,000 Arabic tweets related to online learning. The overall goal is to use sentiment analysis of tweets to find patterns that help improve the quality of online learning. The data set that was collected has three classes 'Positive,' 'Negative,' and 'Neutral.' Crossvalidation is used to run the experiments ten times. Precision, recall, and F-measure was used to measure how well the algorithms worked. Classifiers, such as Support Vector Machines, K nearest neighbors, and Random Forest, were used to classify the dataset. Moreover, a detailed analysis and comparison of the results are made in this research. Finally, a visual examination of the data is made using the word cloud technique. © 2022 IEEE.
machine learning; online learning; opinion mining; sentiment analysis; Classification (of information); COVID-19; E-learning; Forestry; Learning systems; Nearest neighbor search; Quality control; Random forests; Support vector machines; Data set; Education process; Global health; Health emergencies; Machine learning algorithms; Machine-learning; Saudi Arabia
Full text:
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Collection:
Databases of international organizations
Database:
Scopus
Language:
English
Journal:
5th National Conference of Saudi Computers Colleges, NCCC 2022
Year:
2022
Document Type:
Article
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