Technology-Enhanced Systemic Quality Assurance with the Aid of Text-Based Emotion Recognition of Facebook Comments for Higher Education Institutions

Abstract

One of the difficult and recently-emerging problems in the realm of natural language processing is the recognition and analysis of emotions (NLP). A current area of research involves identifying a person's emotional state through textual data in addition to recognizing emotions from face and auditory records. Numerous disciplines, including higher education institutions, can use the study of emotions to their advantage. This is especially true given the widespread usage of social media in today's world, when everything is done online. This information could be very helpful in guiding an organization's decisions. The abundant text found in social media, blogs, and other places can be used to explore different text mining findings, such as emotions. This study tackles on making use of the vast amount of information available online especially in social media platforms through the development of iMosyon, an emotion recognition system to help aid higher institutions in their decision-making process. Experimental results were executed and researchers then decided to use the Support Vector Machine (SVM) model due to the fact that it received the highest accuracy score of 78% among the classifiers. The beta testing which made use of Evaluation of Performance Functionality and Software Product Quality shows an overall rating of 4.5 out of 5.0 that indicates that the respondents accepted its functionality and the feedback was good.