Detecting Emotions in Students' Generated Content: An Evaluation of EmoTect System

Abstract

In this paper, an e-counselling system for automatic detection of emotion in text is evaluated by comparing with two of text classifiers implemented in WEKA machine learning software. A support vector machine classifier was used for the development of the e-counselling system, hence we compared the performance of the e-counselling system’ classifier with the WEKA’s Multinomial naïve-Bayes and J48 decision tree classifiers. While this paper is geared towards ascertaining the efficacy of the various classifiers for classifying emotions in learners’ generated text content, this paper also aims to ascertain the performance of the e-counselling system for complementing decision making concerning students in counselling delivery. In building the system, an annotated students’ life story corpus was developed and used for the experiment. Therefore, 85% of the total instances of the life stories was used as training data while the remaining 15% was used as test data with sample instances of real-time data from students’ textual submission through the e-counselling system. The results of the experiment show that the SVM, implemented in our proposed e-counselling system, is superior over the MNB and J48 classifiers.