Open Access

A New Opinion Mining Method based on Fuzzy Classifier and Particle Swarm Optimization (PSO) Algorithm


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1. Ravi, K., V. Ravi. A Survey on Opinion Mining and Sentiment Analysis: Tasks, Approaches and Applications. – Knowledge-Based Systems, Vol. 89, 2015, pp. 14-46.10.1016/j.knosys.2015.06.015Search in Google Scholar

2. Balazs, J. A., J. D. Velásquez. Opinion Mining and Information Fusion: A Survey. – Information Fusion, Vol. 27, 2016, pp. 95-110.10.1016/j.inffus.2015.06.002Search in Google Scholar

3. Basari, A. S. H., B. Hussin, I. G. P. Ananta, J. Zeniarja. Opinion Mining of Movie Review Using Hybrid Method of Support Vector Machine and Particle Swarm Optimization. – Procedia Engineering, Vol. 53, 2013, pp. 453-462.10.1016/j.proeng.2013.02.059Search in Google Scholar

4. Ye, Q., Z. Zhang, R. Law. Sentiment Classification of Online Reviews to Travel Destinations by Supervised Machine Learning Approaches. – Expert Systems with Applications, Vol. 36, 2009, No 3, pp. 6527-6535.10.1016/j.eswa.2008.07.035Search in Google Scholar

5. Virmani, D., V. Malhotra, R. Tyagi. Sentiment Analysis Using Collaborated Opinion Mining. – arXiv preprint arXiv:1401.2618., 2014.Search in Google Scholar

6. Zadeh, L. A. Fuzzy Sets. – Information and Control, Vol. 8, 1965, No 3, pp. 338-353.10.1016/S0019-9958(65)90241-XSearch in Google Scholar

7. Jebaseeli, A. N., E. Kirubakaran. Genetic Optimized Neural Network Algorithm to Improve Classification Accuracy for Opinion Mining of m-Learning Reviews. – IJETTCS, Vol. 2, 2013, No 3, pp. 345-349.Search in Google Scholar

8. Jusoh, S., H. M. Alfawareh. Applying Fuzzy Sets for Opinion Mining. – In: IEEE International Conference on Computer Applications Technology (ICCAT’13), 2013, pp. 1-5.10.1109/ICCAT.2013.6521965Search in Google Scholar

9. Bagheri, A., M. Saraee, F. de Jong. An Unsupervised Aspect Detection Model for Sentiment Analysis of Reviews. – In: International Conference on Application of Natural Language to Information Systems. Berlin, Heidelberg, Springer, 2013, pp. 140-151.10.1007/978-3-642-38824-8_12Search in Google Scholar

10. Stylios, G., C. D. Katsis, D. Christodoulakis. Using Bio-Inspired Intelligence for Web Opinion Mining. – International Journal of Computer Applications, Vol. 87, 2014, No 5.10.5120/15207-3610Search in Google Scholar

11. Kalaivani, P., K. L. Shunmuganathan. An Improved k-Nearest-Neighbor Algorithm Using Genetic Algorithm for Sentiment Classification. – In: 2014 IEEE International Conference on Circuit, Power and Computing Technologies (ICCPCT’14), March 2014, pp. 1647-1651.10.1109/ICCPCT.2014.7054826Search in Google Scholar

12. Dalal, M. K., M. A. Zaveri. Opinion Mining from Online User Reviews Using Fuzzy Linguistic Hedges. – Applied Computational Intelligence and Soft Computing, 2014, No 2.10.1155/2014/735942Search in Google Scholar

13. Sumathi, T., S. Karthik, M. Marikkannan. Artificial Bee Colony Optimization for Feature Selection in Opinion Mining. – Journal of Theoretical & Applied Information Technology, Vol. 66, 2014, No 1.Search in Google Scholar

14. Rahmath, P., T. Ahmad. Fuzzy Based Sentiment Analysis of Online Product Reviews Using Machine Learning Techniques. – International Journal of Computer Applications, Vol. 99, 2014, No 17, pp. 9-16.10.5120/17463-8243Search in Google Scholar

15. Kalaivani, P., K. L. Shunmuganathan. Feature Reduction Based on Genetic Algorithm and Hybrid Model for Opinion Mining. – Scientific Programming, Vol. 12, 2015.10.1155/2015/961454Search in Google Scholar

16. Bilal, M., H. Israr, M. Shahid, A. Khan. Sentiment Classification of Roman-Urdu Opinions Using Naïve Bayesian, Decision Tree and k-NN Classification Techniques. – Journal of King Saud University-Computer and Information Sciences, Vol. 28, 2016, No 3, pp. 330-344.10.1016/j.jksuci.2015.11.003Search in Google Scholar

17. Bagheri, A., M. Saraee, F. de Jong. ADM-LDA: An Aspect Detection Model Based on Topic Modelling Using the Structure of Review Sentences. – Journal of Information Science, Vol. 40, 2014, No 5, pp. 621-636.10.1177/0165551514538744Search in Google Scholar

18. Chawla, N. V., K. W. Bowyer, L. O. Hall, W. P. Kegelmeyer. SMOTE: Synthetic Minority Over-Sampling Technique. – Journal of Artificial Intelligence Research, Vol. 16, 2002, pp. 321-357.10.1613/jair.953Search in Google Scholar

19. Mendel, J. M. Uncertain Rule-Based Fuzzy Logic Systems: Introduction and New Directions. Upper Saddle River, Prentice Hall PTR, 2001, pp. 131-184.Search in Google Scholar

20. Ishibuchi, H., Y. Nojima. Pattern Classification with Linguistic Rules. – In: Fuzzy Sets and Their Extensions: Representation, Aggregation and Models, 2008, pp. 377-395.10.1007/978-3-540-73723-0_19Search in Google Scholar

eISSN:
1314-4081
Language:
English
Publication timeframe:
4 times per year
Journal Subjects:
Computer Sciences, Information Technology