KOMPARASI FITUR SELEKSI PADA ALGORITMA SUPPORT VECTOR MACHINE UNTUK ANALISIS SENTIMEN REVIEW

Yoseph Tajul Arifin

doi:10.31294/ji.v3i2.868

KOMPARASI FITUR SELEKSI PADA ALGORITMA SUPPORT VECTOR MACHINE UNTUK ANALISIS SENTIMEN REVIEW

Yoseph Tajul Arifin

Abstract

ABSTRAK

The main problem in the process sentiment analysis review is how to choose and use the best feature selection to get the maximal result. The accuracy of the use of algorithm in analysis sentiment review also have been an important role in the determination results of the analysis. Analysis of the sentiment is a study computing on an opinion, behavior and emotion of a person to an entity. This research also discussed comparative studies, technique classification and combining method of the feature selection to comparsion result of the people opinion about tourist destination. The classifications technique to analyze sentiment review of the tourist destinations, using support vector machine algorithm (svm) and a model of the features selection will be compared between a particle swarm optimization and genetic algorithm to increase the accuracy classifications of support vector machines algorithm. The measurement of were based on accuracy support vector machines before and after the addition of features. The evaluation uses 10 cross fold validation. While the measurement of accuracy measured by confusion the matrix and a curve roc. The result showed an increase in accuracy support vector machines of 75.33 % to 88.67 %.

Keywords: Sentimen Review, Support Vector Machine, Analysis Review, Feature Selection.

Full Text:

PDF (Bahasa Indonesia)

References

Basari, A. S. H., Hussin, B., Ananta, I. G. P., & Zeniarja, J. (2013). Opinion Mining of Movie Review using Hybrid Method of Support Vector Machine and Particle Swarm Optimization. Procedia Engineering, 53, 453–462. doi:10.1016/j.proeng.2013.02.059

Chou, J.-S., Cheng, M.-Y., Wu, Y.-W., & Pham, A.-D. (2014). Optimizing parameters of support vector machine using fast messy genetic algorithm for dispute classification. Expert Systems with Applications, 41(8), 3955–3964. doi:10.1016/j.eswa.2013.12.035

Dehkharghani, R., Mercan, H., Javeed, A., & Saygin, Y. (2014). Sentimental causal rule discovery from Twitter. Expert Systems with Applications, 41(10), 4950–4958. doi:10.1016/j.eswa.2014.02.024

Habernal, I., Ptáček, T., & Steinberger, J. (2014). Supervised sentiment analysis in Czech social media. Information Processing & Management, 50(5), 693–707. doi:10.1016/j.ipm.2014.05.001

Haddi, E., Liu, X., & Shi, Y. (2013). The Role of Text Pre-processing in Sentiment Analysis. Procedia Computer Science, 17, 26–32. doi:10.1016/j.procs.2013.05.005

Han, J., & Kamber, M. (2007). Data Mining Concepts and Techniques. San Francisco: Diane Cerra.

Haupt, R. L., & Haupt, S. E. (2004). Practical Genetic Algorithms. Untied States Of America: A John Wiley & Sons Inc Publication.

Huang, K., Yang, H., King, I., & Lyu, M. (2008). Machine Learning Modeling Data Locally And Globally. Berlin Heidelberg: Zhejiang University Press, Hangzhou And Springer-Verlag Gmbh.

Khan, F. H., Bashir, S., & Qamar, U. (2014). TOM: Twitter opinion mining framework using hybrid classification scheme. Decision Support Systems, 57, 245–257. doi:10.1016/j.dss.2013.09.004

Khan, K., Baharudin, B., & Khan, A. (2014). Mining Opinion Components from Unstructured Reviews: A Review. Journal of King Saud University - Computer and Information Sciences. doi:10.1016/j.jksuci.2014.03.009

Litchfield, Steve. (2010). Defining the Smartphone. (n.d.)

Liu, Y., Wang, G., Chen, H., Dong, H., Zhu, X., & Wang, S. (2011). An Improved Particle Swarm Optimization for Feature Selection. Journal of Bionic Engineering, 8(2), 191–200. doi:10.1016/S1672-6529(11)60020-6

Maimon, O. (2010). Data Mining And Knowledge Discovery Handbook. New York Dordrecht Heidelberg London: Springer.

Medhat, W., Hassan, A., & Korashy, H. (2014). Sentiment analysis algorithms and applications: A survey. Ain Shams Engineering Journal. doi:10.1016/j.asej.2014.04.011

Moraes, R., Valiati, J. F., & Gavião Neto, W. P. (2013). Document-level sentiment classification: An empirical comparison between SVM and ANN. Expert Systems with Applications, 40(2), 621–633. doi:10.1016/j.eswa.2012.07.059

Review Text. (n.d). Juli 15, 2015. http://www.ef.co.id/englishfirst/englishstudy/pengertian-dan-struktur-review-text.aspx

Trend Bisnis 2014. (2015). http://www.ekonomi holic.com/2014/01/trend-bisnis-2014.html?m=0

Vercellis, C. (2009). Business Intelligence Data Mining And Optimization For Decision Making .United Kingdom: A John Wiley And Sons, Ltd.,Publication.

Weiss, S. M., Indurkhya, Nitin & Zhang, Tong. (2010). Fundamentals of Predictive Text Mining. London: Springer-Verlag

Witten, H. I., Frank, E., & Hall, M. A. (2011). Data Mining Practical Machine Learning Tools And Technique. Burlington: Elsevier Inc

Ye, Q., Zhang, Z., & Law, R. (2009). Sentiment classification of online reviews to travel destinations by supervised machine learning approaches. Expert Systems with Applications, 36(3), 6527–6535. doi:10.1016/j.eswa.2008.07.035

Zhang, L., Hua, K., Wang, H., Qian, G., & Zhang, L. (2014). Sentiment Analysis on Reviews of Mobile Users. Procedia Computer Science, 34, 458–465. doi:10.1016/j.procs.2014.07.013

Zhang, Z., Ye, Q., Zhang, Z., & Li, Y. (2011). Sentiment classification of Internet restaurant reviews written in Cantonese. Expert Systems with Applications, 38(6), 7674–7682. doi:10.1016/j.eswa.2010.12.147

Zhao, M., Fu, C., Ji, L., Tang, K., & Zhou, M. (2011). Feature selection and parameter optimization for support vector machines: A new approach based on genetic algorithm with feature chromosomes. Expert Systems with Applications, 38(5), 5197–5204. doi:10.1016/j.eswa.2010.1

DOI: https://doi.org/10.31294/ji.v3i2.868