Fine-grained affect detection in learners’ generated content using machine learning

Learners’ adaptation to academic trajectory is shaped by several influencing factors that ought to be considered while attempting to design an intervention towards improving academic performance. Emotion is one factor that influences students’ academic orientation and performance. Tracking emotions in text by psychologists have long been a subject of concern to researchers. This is due to the challenges associated with determining the level of accuracy and consistency of decisions made from analysing such text by psychologists. Lately, Artificial Intelligence has complemented human efforts in tracking emotions in text. This paper provides an overview of machine learning application for detecting emotions in text through a Support vector machine learning system. In addition, we compared the performance of the system’s classifier to WEKA’s Multinomial Naïve-Bayes and J48 decision tree classifiers. Real time data from using the system in counselling delivery and collected students’ life stories were used for evaluating the performance of the classifiers. The evaluation results show that the Support vector machine, implemented in our system, is superior over WEKA’s Multinomial Naïve-Bayes and J48 decision tree classifiers. Nevertheless, the various classifiers performed beyond the acceptable threshold. The implication for the findings goes to indicate that machine learning algorithms can be implemented to track emotions in text, especially from students generated content.