A deep learning based approach for classifying tweets related to online learning during the Covid-19 pandemic

• Published in: Education and information technologies Vol. 29; no. 7; pp. 7707 - 7736
• Main Authors: Senadhira, K. I., Rupasingha, R. A. H. M., Kumara, B. T. G. S.
• Format: Journal Article
• Language: English
• Published: New York Springer US 01-05-2024; Springer Nature B.V
• Subjects: Computer Appl. in Social and Behavioral Sciences; Computer Science; Computers and Education; COVID-19; Deep learning; Distance learning; Education; Educational Practices; Educational Technology; Electronic Learning; Information Systems Applications (incl.Internet); Learning Processes; Pandemics; Social networks; Support vector machines; User Interfaces and Human Computer Interaction; COVID-19; Deep learning; Tweets; Online learning; Classification
• ISSN: 1360-2357; 1573-7608
• DOI: 10.1007/s10639-023-12122-8

The majority of educational institutions around the world have switched to online learning due to the COVID-19 pandemic. Since continuing education has become important during the pandemic as well, academics and students have recognized the value of online learning to avoid their challenges. The objective of this study is to categorize peoples' opinions and determine how the community used online learning during the pandemic. A total of 13,155 tweets were collected using the Twitter API. Of these, 4486 were positive about the online learning process, 4490 were negative, and 4179 were advertising for online learning. After pre-processing the tweets, Term Frequency-Inverse Document Frequency (TF-IDF) vectorizer is used to extract the feature vectors. The data was divided into three categories using the Long Short Term Memory (LSTM) and Support Vector Machine (SVM) algorithms. Sentiment analysis is used to determine how society feels about the online learning process by analyzing positive, negative, and advertisement sentiments. According to the results, LSTM beat SVM and achieved an accuracy of 88.58%. It also achieved higher precision, recall, f-measure values, and lowest error rates for 65% of the training dataset. Based on the findings, the significance of online learning as well as the absence of technologies, the internet, and other subpar educational practices were determined. It was determined that more workable solutions were needed in order to improve online education globally.