Opinion Mining for Predicting Peer Affective Feedback Helpfulness

Mouna Selmi, Hicham Hage, Esma Aïmeur


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 Naïve-Bayes provides the best performance with a prediction accuracy of 87.19%.


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Paper Citation

in Harvard Style

Selmi M., Hage H. and Aïmeur E. (2014). Opinion Mining for Predicting Peer Affective Feedback Helpfulness . In Proceedings of the International Conference on Knowledge Management and Information Sharing - Volume 1: KMIS, (IC3K 2014) ISBN 978-989-758-050-5, pages 419-425. DOI: 10.5220/0005158704190425

in Bibtex Style

author={Mouna Selmi and Hicham Hage and Esma Aïmeur},
title={Opinion Mining for Predicting Peer Affective Feedback Helpfulness},
booktitle={Proceedings of the International Conference on Knowledge Management and Information Sharing - Volume 1: KMIS, (IC3K 2014)},

in EndNote Style

JO - Proceedings of the International Conference on Knowledge Management and Information Sharing - Volume 1: KMIS, (IC3K 2014)
TI - Opinion Mining for Predicting Peer Affective Feedback Helpfulness
SN - 978-989-758-050-5
AU - Selmi M.
AU - Hage H.
AU - Aïmeur E.
PY - 2014
SP - 419
EP - 425
DO - 10.5220/0005158704190425