Volume 18, No. 6, 2021
“Sentiments Of Social Media Users, Towards Web Education – A Data Mining Techniques.”
Dr. Baig Muntajeeb Ali
Abstract
In the 21 st century, the Internet has caused a knowledge explosion, and every day quintillion bytes of data are created. Thus analyzing data and interpreting it has become an arduous task for educators. However, data analysis has become straightforward due to the advent of data mining techniques and machine learning. Recently the Corona Virus pandemic harmed the education system across the World, and the task of imparting education has been shifted from traditional face-to-face education to web education. Websites and social media in teaching and learning have created massive data. It has become a vital source of information for studying social media users' thoughts, emotions, feelings, opinions, and perceptions. Since many social media are available on Internet, this study uses the microblogging Twitter platform for sentiment analysis due its short messages and popularity. In this research, text mining is done using the Open Source Visual Programming Software Orange (3.28.0; Orange Data Mining, n.d.). The text mining technique involves four processes; in the first process, the text is extracted from posts of the Twitter platform; in the second process, the obtained data is preprocessed, and sentiments of Twitter are analyzed with the help of Orange (3.28.0; Orange Data Mining, n.d.) software. In the third step, the classification of sentiments is done using the V.A.D.E.R. (Valence Aware Dictionary and Sentiment Reasoner) modules. Finally, the obtained scores from text mining are used to analyze data. The research findings suggest that Twitter users' sentiments have moderate positive compound sentiment (.389) about web education and the use of technology in education. Further, the compound polarity scores of 38 % of tweets about web education suggest the tweets were moderately positive and greater than zero. This study's moderate positive compound sentiments are due to the shift from traditional face-to-face (f2f) education to Web-based education during the pandemic. The educators, schools, colleges, and universities emphasized web-based teaching, learning, and evaluation during pandemics.
Pages: 527-537
Keywords: Opinion Analysis, Sentiment Analysis, Pre-processing, Data Mining Techniques, Text Mining Techniques, Machine Learning Approach, and Social Networking Sites (S.N.S.).