Analisa Sentimen Review Hotel Menggunakan Algoritma Support Vector Machine Berbasis Particle Swarm Optimization

Elly Indrayuni

Abstract


Abstract - Hotel is one of the most important tourism product to be considered both in terms of facilities, services or mileage and travel. We have had many travel websites that provide the facility for internet users write opinions and personal experiences online. Sentiment analysis or opinion mining is one solution to overcome the problem of classifying opinions or reviews into positive or negative opinion automatically. The technique used in this study is Support Vector Machines (SVM). SVM is able to identify the separate hyperplane that maximizes the margin between two different classes. The accuracy of the resulting value will be the benchmark to find the best test model for sentiment classification case. The evaluation was done using 10 fold cross validation. The results showed an increase in accuracy of 5.61% for Support Vector Machine algorithm from 91.33% to 96.94% after the application of selection features Particle Swarm Optimization.
Keywords: Sentiment Analysis, Review, Support Vector Machines, Particle Swarm Optimization



Abstrak - Hotel merupakan salah satu produk pariwisata yang sangat penting untuk dipertimbangkan baik dari segi fasilitas, pelayanan ataupun jarak tempuh perjalanan wisata. Saat ini sudah banyak website wisata yang menyediakan fasilitas untuk pengguna internet menuliskan opini dan pengalaman pribadinya secara online. Analisa sentimen atau opinion mining merupakan salah satu solusi mengatasi masalah untuk mengelompokan opini atau review menjadi opini positif atau negatif secara otomatis. Metode yang digunakan pada penelitian ini adalah Support Vector Machine. SVM mampu mengidentifikasi hyperplane terpisah yang memaksimalkan margin antara dua kelas yang berbeda. Nilai akurasi yang dihasilkan akan menjadi tolak ukur untuk mencari model pengujian terbaik untuk kasus klasifikasi sentimen. Evaluasi dilakukan menggunakan 10 fold cross validation. Hasil penelitian menunjukkan peningkatan nilai akurasi sebesar 5.61% untuk algoritma Support Vector Machine dari 91.33% menjadi 96.94% setelah penerapan seleksi fitur Particle Swarm Optimization.
Kata Kunci: Analisa sentimen, Review, Support Vector Machines, Particle Swarm Optimization

Full Text:

PDF

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

Dhande, L. L., dan Patnaik, G. K., 2014. Analyzing Sentiment of Movie Review Data using Naive Bayes Neural Classifier. International Journal of Emerging Trends & Technology in Computer Science (IJETTCS), vol (3) Issue 4. ISSN 2278-6856.

Fawcett, Tom. 2005. An introduction to ROC Analysis. Pattern Recognition Letters, 27, 861-874. doi:10.1016/j.patrec.2005.10.010

Gencosman, B. C., Ozmutlu, H. C., dan Ozmutlu, S. 2014. Character n-gram application for automatic new topic identification. Information Processing and Management, 50, 821-856. doi:10.1016/j.ipm.2014.06.005

Ghiassi, M., Skinner, J., dan Zimbra, D. (2013). Twitter brand sentiment analysis: A hybrid system using n-gram analysis and dynamic artificial neural network. Expert Systems with Applications, 40, 6266-6282. doi:10.1016/j.eswa.2013.05.057

Haddi, E., Liu, X., dan 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.

Kang, H., Yoo, J.S., dan Han, D. 2012. Senti-lexicon and improved Naive Bayes algorithms for sentiment analysis of restaurant reviews. Expert Systems with Applications, 39, 6000-6010. doi:10.1016/j.eswa.2011.11.107

Kontopoulos, E., Berberidis, C., Dergiades, T., dan Bassiliades, N. 2013. Ontology-based sentiment analysis of twitter post. Expert Systems with Applications, 40, 4065-4074. doi:10.1016/j.eswa.2013.01.001

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

Mitra, V., Wang, C. J., dan Banerjee, S. 2007. Text classification: A least square support vector machine approach. Applied Soft Computing, 7, 908-914. doi:10.1016/j.asoc.2006.04.002

Moraes, R., Valiati, J. F., dan 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

Patil, G., Galande, V., Kekan, V., dan Dange, K. 2014. Sentiment Analysis using Support Vector Machine. International Journal of Innovative Research in Computer and Communication Engineering.

Taylor, E. M., Velasquez, J. D., Marquez, F. B., dan Matsuo, Y., 2013. Indentifying Customer Preferences about Tourism Products using an Aspect-Based Opinion Mining Approach. Procedia Computer Science, 22, 182-191. doi:10.1016/j.procs.2013.09.094

Ye, Q., Zhang, Z., dan 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, 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

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

Akh. Fajar Rahman, Anton Triyantoro, Suradi. Pengaruh Kualitas Produk Dan Pelayanan Terhadap Kepuasan Konsumen Serta Dampaknya Terhadap Keputusan Konsumen Menginap Kembali. Vol 7, No 1 (2016): Jurnal Khasanah Ilmu - Maret 2016

Desi Supriyati Pembangunan Sistem Informasi Apotek Dharma Sehat Donorojo. Vol 2, No 1 (2016): IJSE 2016

Muhammad Arsyad. Sistem Pendukung Keputusan Untuk Seleksi Calon Ketua Badan Eksekutif Mahasiswa (BEM) STMIK Banjarbaru Dengan Metode Weighted Product (WP). Vol 4, No 1 (2016): Jurnal Bianglala Informatika 2016

Rahmat Hidayat. Menentukan Promosi Jabatan Karyawan Dengan Menggunakan Metode Profile Matching Dan Metode Promethee. Vol 2, No 1 (2016): IJSE 2016

Nika Nofiana. Sistem Pendukung Keputusan Untuk Pemberian Ijin Usaha Penambangan Pada Dinas Pertambangan Dan Energi Kab. Pacitan. Vol 6, No 2 (2014): Jurnal Speed 22 – 2014

Riesda Ganevi, Bambang Eka Purnama. Sistem Pendukung Keputusan Penilaian Kinerja Guru Sekolah Menengah Pertama Negeri (SMP N) 1 Pacitan. Vol 6, No 4 (2014): Jurnal Speed 24 – 2014




DOI: https://doi.org/10.31294/evolusi.v4i2.697

ISSN: 2657-0793 (online). ISSN: 2338-8161 (print)

Published By LPPM Universitas Bina Sarana Informatika

Jl. Kramat Raya No.98, Kwitang, Kec. Senen, Kota Jakarta Pusat, DKI Jakarta 10450
Creative Commons License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License