A Novel Sentimental Analysis using Optimized Relevance Vector Machine Classifier


  • N. Saranya
  • R. Gunavathi
  • . .






Optimization, Vector Machine Classifier, Cuckoo Search Optimization


Sentimental analysis is the process of identifying the human’s thoughts or feelings. So Many methods have been developed for the sentimental analysis. Machine learning is one of the widely used approaches towards sentiment classification. In this work, Sentimental analysis is done by using Relevance Vector Machine Classifier with Cuckoo Search Optimization. Here Relevance Vector Machine Classifier (RVMC) is combined with Cuckoo Search Optimization (CSO) for better accuracy and performance. Experiment is made with movie and twitter datasets. Accuracy, precision and recall of all other techniques are evaluated. Here the comparison is made among other algorithms. The result shows that RVMC-CSO algorithm gives accuracy and good performance than other algorithm like SVM, ELM and RVM.




[1] Hwang, S. and Jeong, M.K., 2018. Robust relevance vector machine for classification with variational inference. Annals of Operations Research, 263(1-2), pp.21-43.

[2] Yu, H., Wu, Z., Chen, D. and Ma, X., 2017. Probabilistic prediction of bus headway using relevance vector machine regression. IEEE Transactions on Intelligent Transportation Systems, 18(7), pp.1772-1781.

[3] Tipping, M.E., 2000. The relevance vector machine. In Advances in neural information processing systems (pp. 652-658).

[4] Foody, G.M., 2008. RVMâ€based multiâ€class classification of remotely sensed data. International Journal of Remote Sensing, 29(6), pp.1817-1823.

[5] Rafi, M. and Shaikh, M.S., 2013. A comparison of SVM and RVM for Document Classification. arXiv preprint arXiv:1301.2785.

[6] P. Byuvol, L. Gabsalikhova, I. Makarova, E. Mukhametdinov, G. Sadygova, “Improving the Branded Service Network Efficiency based on its Functioning Evaluationâ€, Astra Salvensis, Supplement No. 2, p. 373, 2017.

[7] Azad N., Ghandvar P., Rahimi Z., “Online Search Behaviour of Customers in Shoe Marketâ€, Astra Salvensis, Supplement No. 2, p. 793, 2017.

[8] Tzikas, D.G., Wei, L., Likas, A., Yang, Y. and Galatsanos, N.P., 2006. A tutorial on relevance vector machines for regression and classification with applications. EURASIP News Letter, 17(2), p.4.

[9] Zou, X., Yang, J. and Zhang, J., 2018. Microblog sentiment analysis using social and topic context. PloS one, 13(2), p.e0191163.

[10] Khan, M.U., Nanopoulos, A. and Schmidt-Thieme, L., 2015. P2P RVM for Distributed Classification. In Data Science, Learning by Latent Structures, and Knowledge Discovery (pp. 145-155). Springer, Berlin, Heidelberg.

[11] Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., Mohammad, A.S., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O. and Hoste, V., 2016. SemEval-2016 task 5: Aspect based sentiment analysis. In Proceedings of the 10th international workshop on semantic evaluation (SemEval-2016) (pp. 19-30).

[12] Bowd, C., Medeiros, F.A., Zhang, Z., Zangwill, L.M., Hao, J., Lee, T.W., Sejnowski, T.J., Weinreb, R.N. and Goldbaum, M.H., 2005. Relevance vector machine and support vector machine classifier analysis of scanning laser polarimetry retinal nerve fiber layer measurements. Investigative ophthalmology & visual science, 46(4), pp.1322-1329.

[13] Jadav, B.M. and Vaghela, V.B., 2016. Sentiment analysis using support vector machine based on feature selection and semantic analysis. International Journal of Computer Applications, 146(13).

[14] Huang, X., Shi, L. and Suykens, J.A., 2014. Asymmetric least squares support vector machine classifiers. Computational Statistics & Data Analysis, 70, pp.395-405.

[15] BholaneSavita, D. and Gore, D., 2016.Sentiment Analysis on Twitter Data Using Support Vector Machine. International Journal of Computer Science Trends and Technology (IJCST)–Volume, 4, p.365.

[16] Saranya, N. and Gunavathi, R., 2017. Sentimental analysis using least squares twin support vector machine. International Journal of Advanced Research in Computer Science, 8(7).

[17] Hoang, N.D. and Tien Bui, D., 2016. A novel relevance vector machine classifier with cuckoo search optimization for spatial prediction of landslides. Journal of Computing in Civil Engineering, 30(5), p.04016001.

[18] Taboada, M., Brooke, J., Tofiloski, M., Voll, K. and Stede, M., 2011. Lexicon-based methods for sentiment analysis. Computational linguistics, 37(2), pp.267-307.

[19] Ceron, A., Curini, L., Iacus, S.M. and Porro, G., 2014. Every tweet counts? How sentiment analysis of social media can improve our knowledge of citizens’ political preferences with an application to Italy and France. New Media & Society, 16(2), pp.340-358.

[20] Pestian, J.P., Matykiewicz, P., Linn-Gust, M., South, B., Uzuner, O., Wiebe, J., Cohen, K.B., Hurdle, J. and Brew, C., 2012. Sentiment analysis of suicide notes: A shared task. Biomedical informatics insights, 5, pp.BII-S9042.

View Full Article:

How to Cite

Saranya, N., Gunavathi, R., & ., . (2018). A Novel Sentimental Analysis using Optimized Relevance Vector Machine Classifier. International Journal of Engineering & Technology, 7(4.7), 164–166. https://doi.org/10.14419/ijet.v7i4.7.20536
Received 2018-09-29
Accepted 2018-09-29
Published 2018-09-27