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Authors: Mouna Selmi 1 ; Hicham Hage 2 and Esma Aïmeur 1

Affiliations: 1 Université de Montréal, Canada ; 2 Notre Dame University, Lebanon

Keyword(s): e-Learning, Peers’ Interaction, Peer Affective Feedback, Classification, Machine Learning, Natural Language Processing, Sentiment Analysis, Opinion Mining.

Related Ontology Subjects/Areas/Topics: Applications ; Artificial Intelligence ; Communication, Collaboration and Information Sharing ; e-Business ; Education/Learning ; e-Learning ; Enterprise Information Systems ; Human-Computer Interaction ; Knowledge Management and Information Sharing ; Knowledge-Based Systems ; Symbolic Systems

Abstract: 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 Engl ish 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%. (More)

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Paper citation in several formats:
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 - KMIS, (IC3K 2014) ISBN 978-989-758-050-5; ISSN 2184-3228, pages 419-425. DOI: 10.5220/0005158704190425

@conference{kmis14,
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 - KMIS, (IC3K 2014)},
year={2014},
pages={419-425},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005158704190425},
isbn={978-989-758-050-5},
issn={2184-3228},
}

TY - CONF

JO - Proceedings of the International Conference on Knowledge Management and Information Sharing - KMIS, (IC3K 2014)
TI - Opinion Mining for Predicting Peer Affective Feedback Helpfulness
SN - 978-989-758-050-5
IS - 2184-3228
AU - Selmi, M.
AU - Hage, H.
AU - Aïmeur, E.
PY - 2014
SP - 419
EP - 425
DO - 10.5220/0005158704190425

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