The week's pick
Random Articles
Reseach Article
Opinion Analysis Model for Classification of Opinions for M Learning
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 109 - Number 16 |
| Year of Publication: 2015 |
| Authors: A.Nisha Jebaseeli |
10.5120/19411-1066
|
A.Nisha Jebaseeli . Opinion Analysis Model for Classification of Opinions for M Learning. International Journal of Computer Applications. 109, 16 ( January 2015), 51-54. DOI=10.5120/19411-1066
Abstract
The main objective of this research is to propose an Opinion Analysis Model for classification of opinions with the evolutionary approach and to improve the classification accuracy. The research presented three proposals to heed to various real-world problems. Some processes were posed, described, discussed and compared with existing classification algorithms. An improvement of classification accuracy is noticed when the data is preprocessed by KN preprocessing algorithm. Thus, the formed corpuses were applied in the existing classification algorithms which showed an increase in the accuracy rates. The enrichment of the classification algorithms with the neural network and genetic optimization brought a significant intervention in the classification accuracies. The research, for analyzing, took into account Mobile Learning (M-Learning) dataset from Android Market website. In order to assess the effectiveness of the proposed algorithms, the benchmark experiments are conducted with Internet Movie Database (IMDb) dataset comprising of movie reviews.
References
- Chenghua Lin, Yulan He, Richard Everson, Stefan Ruger, “Weakly Supervised Joint Sentiment-Topic Detection from Text”, IEEE Transactions On Knowledge and Data Engineering, Volume 24, Number 6, pp. 1134-1144, 2012.
- Chih-Ming Chen, Shih-Hsun Hsu, “Personalized Intelligent Mobile Learning System for Supporting Effective English Learning”, International Forum of Educational Technology & Society, Volume 11, Issue 3, pp. 153-180, 2008.
- Traxler, J. “Current State of Mobile Learning”, Mobile Learning: Transforming the Delivery of Education and Training. Edmonton: Athabasca University Press, pp. 9-24, 2009.
- Kukulska-Hulme, A., “Practitioners as Innovators: Emergent Practice in Personal Mobile Teaching, Learning, Work, and Leisure”, in Ally, M. (ed.) Mobile Learning: Transforming the Delivery of Education and Training. Edmonton: Athabasca University Press, pp. 135-155, 2009.
- Bo Pang, Lillian Lee, “Opinion mining and sentiment analysis”, Foundations and Trends in Information Retrieval, Volume 2, Number 1-2, pp. 1–135, 2008.
- S.N. Sivanandam, S.N. Deepa, “Principles of Soft computing”, Wiley India(P) Ltd., 2007.
- Weiyang Zhou, “Verification of the Nonparametric Characteristics of Backpropagation Neural Networks for Image Classification”, IEEE Transactions on Geoscience and Remote Sensing, Volume 37, Number 2, 1999.
- D.G. Rees, “Essential statistics”, 4th edition, Chapman and Hall/CRC. ISBN 1-58488-007-4 (section 9.5), 2001
- Karina Gibert, Joaquín Izquierdo, Geoff Holmes, Ioannis Athanasiadis, Joaquim Comas, Miquel Sànchez-Marrè, “On the role of pre and post-processing in environmental data mining”, International Congress on Environmental Modeling and Software Integrating Sciences and Information Technology for Environmental Assessment and Decision Making, pp. 1937-1958, 2008.
- P. Turney, “Thumbs Up or Thumbs Down? Semantic Orientation Applied to Unsupervised Classification of Reviews”, Proceedings of the 40th Annual Meeting on Association for Computational Linguistics, pp. 417-424, 2002.
- A. Rangel-Merino, J. L. Lopez-Bonilla and R. Linares Y. Miranda, Optimization method based on genetic algorithms”, Apeiron, Volume 12, pp. 393-408, 2005,
- Marylyn D Ritchie, Bill C White, Joel S Parker, Lance W Hahn and Jason H Moore, “Optimization of neural network architecture using genetic programming improves detection and modeling of gene-gene interactions in studies of human diseases”, BMC Bioinformatics, Volume 4, Number 28, pp.1-14, 2003
- Kyoung - Jae Kima, Ingoo Hanb, “Application of a hybrid genetic algorithm and neural network approach in activity-based costing”, Expert Systems with Applications, Volume 24, pp. 73–77, 2003
- Gwo-Jen Hwang, Peng-Yeng Yin, Tzu-Ting Wang, Judy C.R. Tseng, Gwo-Haur Hwang “An enhanced genetic approach to optimizing auto-reply accuracy of an e-learning system” Computers and Education, Volume 51, pp. 337-353, 2008,
- Mohamed Ettaouil, Mohamed Lazaar, Youssef Ghanou, “Architecture Optimization Model For The Multilayer Perceptron And Clustering”, Journal of Theoretical and Applied Information Technology, Volume 47, Number 1, pp. 64-72,2013,
- Fadl Mutaher Ba-Alwi, “Knowledge Acquisition Tool for Classification Rules using Genetic Algorithm Approach” International Journal of Computer Applications, Volume 60, Number 1, pp. 29-35, 2012,
- Guoqiang Peter Zhang, “Neural Networks for Classification: A Survey”, IEEE Transactions On Systems, Man, And Cybernetics—Part C: Applications and Reviews, Volume 30 -No. 4, pp. 451-462, 2000.
- Jack G. Conrad, Frank Schilder, “Opinion Mining in Legal Blogs”, Proceedings of the 11th International Conference on Artificial Intelligence and Law, ICAIL, pp.231-236, 2007
Index Terms
Keywords