Multi-aspect sentiment analysis on netflix application using latent dirichlet allocation and support vector machine methods
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Abstract
Among many film streaming platforms that have sprung up, Netflix is the platform that has the most subscribers compared to the other platforms. However, not all reviews provided by the Netflix users are good reviews. These reviews will later be analyzed to determine what aspects are reviewed by the users based on reviews written on the Google Play Store, using the Latent Dirichlet Allocation (LDA) method. Then, the classification process using the Support Vector Machine (SVM) method will be carried out to determine whether each of these reviews is included in the positive or negative class (Sentiment Analysis). There are 2 scenarios that were carried out in this study. The first scenario resulted that the best number of LDA topics to be used is 40, and the second scenario resulted that the use of filtering process in the preprocessing stage reduces the score of the f1-score. Thus, this study resulted in the best performance score on LDA and SVM testing with 40 topics, and without running the filtering process with the score of 78.15%.
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How to Cite
[1]
A. Abelard and Y. Sibaroni, “Multi-aspect sentiment analysis on netflix application using latent dirichlet allocation and support vector machine methods”, INFOTEL, vol. 13, no. 3, pp. 128-133, Aug. 2021.
Section
Informatics
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References
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[3] A. Agarwal, B. Xie, Ll. Vovsha, O. Rambow, and R. Passonneau, “Sentiment analysis of twitter data,” Proc. Work. Lang. Soc. Media (LSM 2011),", 2011.
[4] F. F. Irfani, “Analisis Sentimen Review Aplikasi Ruangguru Menggunakan Algoritma Support Vector Machine,” JBMI (Jurnal Bisnis, Manajemen, dan Inform., vol. 16, no. 3, pp. 258–266, 2020, doi: 10.26487/jbmi.v16i3.8607.
[5] V. Parkhe and B. Biswas, “Aspect based sentiment analysis of movie reviews: Finding the polarity directing aspects,” Proc. - 2014 Int. Conf. Soft Comput. Mach. Intell. ISCMI 2014, pp. 28–32, 2014, doi: 10.1109/ISCMI.2014.16.
[6] I. Mathilda Yulietha and S. Al Faraby, “Klasifikasi Sentimen Review Film Menggunakan Algoritma Support Vector Machine,” e-Proceeding Eng., vol. 4, no. 3, pp. 4740–4750, 2017.
[7] S. Al Faraby, E. R. R. Jasin, A. Kusumaningrum, and Adiwijaya, “Classification of hadith into positive suggestion, negative suggestion, and information,” J. Phys. Conf. Ser., vol. 971, no. 1, 2018, doi: 10.1088/1742-6596/971/1/012046.
[8] F. F. Rahmawati and Y. Sibaroni, “Multi-Aspect Sentiment Analysis pada Destinasi Pariwisata Yogyakarta Menggunakan Support Vector Machine dan Particle Swarm Optimization sebagai Seleksi Fitur,” 2019.
[9] F. Xianghua, L. Guo, Y. Guo, and W. Zhiqiang, “Multi-aspect sentiment analysis for Chinese online social reviews based on topic modeling and HowNet lexicon,” Knowledge-Based Syst., vol. 37, no. ISSN 0950-7051, pp. 186–195, 2013, [Online]. Available: https://doi.org/10.1016/j.knosys.2012.08.003.
[10] E. Wahyudi and R. Kusumaningrum, “Aspect Based Sentiment Analysis in E-Commerce User Reviews Using Latent Dirichlet Allocation (LDA) and Sentiment Lexicon,” ICICOS 2019 - 3rd Int. Conf. Informatics Comput. Sci. Accel. Informatics Comput. Res. Smarter Soc. Era Ind. 4.0, Proc., pp. 1–6, 2019, doi: 10.1109/ICICoS48119.2019.8982522.
[11] Y. Yiran and S. Srivastava, “Aspect-based Sentiment Analysis on mobile phone reviews with LDA,” ACM Int. Conf. Proceeding Ser., pp. 101–105, 2019, doi: 10.1145/3340997.3341012.
[12] W. C. Widyaningtyas, A. Adiwijaya, and S. Al Faraby, “Klasifikasi Sentiment Analysis Pada Review Film Berbahasa Inggris Dengan Menggunakan Metode Doc2vec Dan Support Vector Machine (svm),” eProceedings Eng., vol. 5, no. 1, pp. 1570–1578, 2018.
[13] K. S. Nugroho, “Dasar Text Preprocessing dengan Python,” 2019. https://medium.com/@ksnugroho/dasar-text-preprocessing-dengan-python-a4fa52608ffe (accessed Nov. 17, 2020).
[14] V. Octriany, “Aspect Based Sentiment Analysis With Combination Feature Extraction LDA and Word2vec,” 2021. Unpublished.
[15] D. M. Blei, A. Y. Ng, and M. T. Jordan, “Latent dirichlet allocation,” Adv. Neural Inf. Process. Syst., no. January 2001, 2002.
[16] F. E. Cahyanti, Adiwijaya, and S. Al Faraby, “On the Feature Extraction for Sentiment Analysis of Movie Reviews Based on SVM,” 2020 8th Int. Conf. Inf. Commun. Technol. ICoICT 2020, 2020, doi: 10.1109/ICoICT49345.2020.9166397.
[17] W. P. Ramadhan, N. Astri, and S. Casi, “Analisis Sentimen Menggunakan Support Vector Machine dan Maximum Entropy,” e-Proceeding Eng., vol. 4, no. 2, pp. 13–14, 2017.
[18] I. Subagyo, L. D. Yulianto, W. Permadi, and A. W. Dewantara, “Sentiment Analisis Review Film Di IMDB Menggunakan Algoritma SVM Sentiment Analysis of Film Review at IMDB using SVM algorithm Abstrak Pendahuluan Metode Penelitian,” vol. 8, no. 1, pp. 47–56, 2019.