Opinion Mining for Predicting Peer Affective Feedback Helpfulness

Mouna Selmi, Hicham Hage, Esma Aïmeur

2014

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 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%.

References

  1. Robinson, J., McQuiggan, S. and Lester, J., 2009. Evaluating the consequences of affective feedback in intelligent tutoring systems. in Affective Computing and Intelligent Interaction and Workshops, 2009. ACII.
  2. Ortigosa, A., José, M. M., Carro R.M., 2014. Sentiment analysis in Facebook and its application to e learning. Computers in Human Behavior 31: pp.527-541.
  3. Lu, J., Law, N. 2012. Online peer assessment: effects of cognitive and affective feedback. Instructional Science, 40(2): pp. 257-275.
  4. Walker, E., Rummel, N., Walker, S., Koedinger, K.R., 2012. Noticing relevant feedback improves learning in an intelligent tutoring system for peer tutoring. in Intelligent Tutoring Systems. Springer.
  5. Selmi, M., Hage, H., Aïmeur, E., 2013. Privacy framework for peer affective feedback, in Proceeding of the 9th International conference on Signal Image technology & Internet based Systems (SITIS 2013), pp. 1049- 1056. IEEE.
  6. Nicol, D.J., Macfarlane-Dick, D., 2006. Formative assessment and self-regulated learning: A model and seven principles of good feedback practice. Studies in Higher Education, 31(2): pp. 199-218.
  7. Nandi, D., Hamilton, M., Harland, J., 2012. Evaluating the quality of interaction in asynchronous discussion forums in fully online courses. Distance Education, 33(1): pp. 5-30.
  8. Rabbany, R., Elatia, S., Takaffoli, M., Zaiane, A.R., 2014. Collaborative Learning of Students in Online Discussion Forums: A Social Network Analysis Perspective, in Educational Data Mining. Springer. pp. 441-466.
  9. Siering, M., Muntermann, J., 2013. What Drives the Helpfulness of Online Product Reviews? From Stars to Facts and Emotions. in Wirtschaftsinformatik.
  10. Lu, Y., Tsaparas, P., Ntoulas, A., Polanyi, L. 2010., Exploiting social context for review quality prediction. in Proceedings of the 19th international conference on World wide web. ACM.
  11. Xiong, W., Litman, D., 2011. Understanding differences in perceived peer-review helpfulness using natural language processing. in Proceedings of the 6th Workshop on Innovative Use of NLP for Building Educational Applications. Association for Computational Linguistics.
  12. Fishbach, A., Zhang, Y., Trope, Y., 2010. Counteractive evaluation: Asymmetric shifts in the implicit value of conflicting motivations. Journal of Experimental Social Psychology, 46(1): pp. 29-38.
  13. Prekopcsák, Z., Makrani, G., Henk, T., Gaspar, C., 2011. Radoop: Analyzing big data with rapidminer and hadoop. in Proceedings of the 2nd RapidMiner Community Meeting and Conference (RCOMM 2011).
  14. Pang, B., Lee, L., Vaithyanathan, S.: Thumbs up? 2002. sentiment classification using machine learning techniques. in Proceedings of the ACL-02 conference on Empirical methods in natural language processing, 10. Association for Computational Linguistics.
  15. Martínez-Cámara, E., Martín-Valdivia, M.T., UreñaLópez, L.A., 2011. Opinion classification techniques applied to a spanish corpus, in Natural Language Processing and Information Systems. Springer. pp. 169-176.
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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

@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 - Volume 1: KMIS, (IC3K 2014)},
year={2014},
pages={419-425},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005158704190425},
isbn={978-989-758-050-5},
}


in EndNote Style

TY - CONF
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