Opinion Mining for Predicting Peer Affective Feedback Helpfulness

Peer feedback has become increasingly popular since the advent of social networks, which has significantly changed the process of learning. Some of today’s e-learning systems enable students to communicate with peers (or co-learners) and ask or provide feedback. However, the highly variable nature of peer feedback makes it difficult for a learner who asked for help to notice and benefit from helpful feedback provided by his peers, especially if he is in emotional distress. Helpful feedback in affective context means positive, motivating and encouraging feedback while an unhelpful feedback is negative, bullying and demeaning feedback. In this paper, we propose an approach to predict the helpfulness of a given affective feedback for a learner based on the feedback content and the learner’s affective state. The proposed approach uses natural language processing techniques and machine learning algorithms to classify and predict the helpfulness of peers’ feedback in the context of an English learning forum. In order to seek the best accuracy possible, we have used several machine learning algorithms. Our results show that Naive-Bayes provides the best performance with a prediction accuracy of 87.19%.