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Opinion Mining of M Learning Reviews using Soft Computing Techniques

International Journal of Computer Applications
© 2012 by IJCA Journal
Volume 54 - Number 15
Year of Publication: 2012
A. Nisha Jebaseeli
E. Kirubakaran

Nisha A Jebaseeli and E Kirubakaran. Article: Opinion Mining of M Learning Reviews using Soft Computing Techniques. International Journal of Computer Applications 54(15):44-48, September 2012. Full text available. BibTeX

	author = {A. Nisha Jebaseeli and E. Kirubakaran},
	title = {Article: Opinion Mining of M Learning Reviews using Soft Computing Techniques},
	journal = {International Journal of Computer Applications},
	year = {2012},
	volume = {54},
	number = {15},
	pages = {44-48},
	month = {September},
	note = {Full text available}


Internet has increasingly become the place for online learning, and exchange of ideas. The rapid development in wireless technology offering fast data transfer has lead to mobile device revolution. With the ease of access of mobile devices like mobile phones, PDAs, tablet PCs and high bandwidth through wireless, there is an upsurge of mobile learning or M-learning. It is important to know the opinion of users using m-learning platforms for developing and fine tuning of M-learning systems. The sheer volume of reviews found in the internet blog spot, bulletin board makes it difficult to track and understand customer opinions. Opinion mining also known as sentiment mining is an area of research which attempts at determining the opinion underlying a text written in natural language which summarizes the customer reviews and express whether the opinions are positive or negative. In this paper, we investigate the classification accuracy of machine learning algorithms for opinion mining of M-learning system review.


  • MoLeNET (2010). Modernising education and training. Mobilising technology for learning, Available at https://crm. lsnlearning. org. uk/user/order. aspx?code=100103
  • Sharples, M. (2009) Methods for Evaluating Mobile Learning. In G. N. Vavoula, N. Pachler, and A. Kukulska-Hulme (eds), Researching Mobile Learning: Frameworks, Tools and Research Designs. Oxford: Peter Lang Publishing Group, pp. 17-39.
  • Schwabe, G. , & Goth, C. (2005). Mobile learning with a mobile game: design and motivational effects. Journal of Computer Assisted Learning, 21(3), 204-216.
  • Damala, A. , & Lecoq, C. (2005 ). Mobivisit: Nomadic Computing in indoor cultural settings. A field study in the museum of Fine Arts, Lyon. In X. Perrot (Ed. ), ICHIM International Cultural Heritage Informatics Meeting September 21-23, 2005, Paris, France
  • Rogers, Y. , Price, S. , Fitzpatrick, G. , Fleck, R. , Harris, E. , Smith, H. , et al. (2004, June 1-3). Ambient wood: designing new forms of digital augmentation for learning outdoors. Paper presented at the 2004 conference on Interaction design and children: building a community (IDC 2004), Maryland, USA.
  • Bing Liu . Exploring User Opinions in Recommender Systems. Proceeding of the second KDD workshop on Large Scale Recommender Systems and the Netflix Prize Competition, Aug 24, 2008, Las Vegas, Nevada, USA.
  • Peter D. Turney and Michael L. Littman. 2003. Measuring praise and criticism: Inference of semantic orientation from association. ACM Transactions on Information Systems, 21(4):315–346.
  • Dave, D. , Lawrence, A. , and Pennock, D. Mining the Peanut Gallery: Opinion Extraction and Semantic Classification of Product Reviews. Proceedings of International World Wide Web Conference (WWW'03), 2003.
  • Turney, P. Thumbs Up or Thumbs Down? Semantic Orientation Applied to Unsupervised Classification of Reviews. ACL'02, 2002.
  • MovieReviewDatahttp://www. cs. cornell. edu/People/pabo/movie-review-data/
  • L. Breiman, "Random forests," Mach. Learning, vol. 45, pp. 5–32, 2001
  • Y. Saeys, T. Abeel, and Y. Van de Peer, "Robust feature selection using ensemble feature selection techniques," in Proc. ECML/PKDD, Part II (LNAI 5212), 2008, pp. 313–325.
  • Freund, Y. and Schapire, R.
  • Experiments with a new boosting algorithm, Machine Learning: Proceedings of the Thirteenth International Conference, pp. 148-156.
  • M. T. Hagan, H. B. Demuth, and M. Beale, "Neural Networks Design", PWS Publishing, 1996