Understanding COVID-19 halal vaccination discourse on facebook and twitter using aspect-based sentiment analysis and text emotion analysis

The COVID-19 pandemic introduced unprecedented challenges for people and governments. Vaccines are an available solution to this pandemic. Recipients of the vaccines are of different ages, gender, and religion. Muslims follow specific Islamic guidelines that prohibit them from taking a vaccine with certain ingredients. This study aims at analyzing Facebook and Twitter data to understand the discourse related to halal vaccines using aspect-based sentiment analysis and text emotion analysis. We searched for the term “halal vaccine” and limited the timeline to the period between 1 January 2020, and 30 April 2021, and collected 6037 tweets and 3918 Facebook posts. We performed data preprocessing on tweets and Facebook posts and built the Latent Dirichlet Allocation (LDA) model to identify topics. Calculating the sentiment analysis for each topic was the next step. Finally, this study further investigates emotions in the data using the National Research Council of Canada Emotion Lexicon. Our analysis identified four topics in each of the Twitter dataset and Facebook dataset. Two topics of “COVID-19 vaccine” and “halal vaccine” are shared between the two datasets. The other two topics in tweets are “halal certificate” and “must halal”, while “sinovac vaccine” and “ulema council” are two other topics in the Facebook dataset. The sentiment analysis shows that the sentiment toward halal vaccine is mostly neutral in Twitter data, whereas it is positive in Facebook data. The emotion analysis indicates that trust is the most present emotion among the top three emotions in both datasets, followed by anticipation and fear

Quantitive Approach to Measuring Course Learning Outcomes

Quantitive Approach to Measuring Course Learning OutcomesAbstract  Accreditation criteria of computing and engineering programs require effective learning outcomes assessment with documented procedures, tools, results, and actions to close the assessment loop with broad faculty involvement. This paper describes a methodology for providing quantitative measurement of a course learning outcomes. The methodology uses a linkage matrix that associate each course learning outcome to one or more course assessment tool. The approach adopted provides a numeric score between 0 and 1 for each learning outcome with respect to each assessment tool and a combined score will be calculated for each learning outcome from the tools associated with that outcome. The proposed methodology also provides insights into the consistency of the various assessment tool used to measure the achievement of a particular course learning outcome. The methodology described here has been successfully adopted in obtaining accreditation to  for various computing degree programs offered by the College of Information Technology at Ajman University of Science & Technology.Keywords: Course outcomes; Course assessment; learning outcomes; Accreditation..