Studi Komparatif Metode Ekstraksi Fitur pada Analisis Sentimen Maskapai Penerbangan Menggunakan Support Vector Machine dan Maximum Entropy
Abstract
Almost all companies use social media to improve their product services and provide after-sales services that allow their customers to review the quality of their products. By using Twitter social media to be an important source for tracking sentiment analysis. Sentiment analysis is one of the most popular studies today, using sentiment analysis companies can analyze customer satisfaction to improve their services. This study aims to analyze airline sentiments with five different features such as pragmatic, lexical n-gram, POS, sentiment, and LDA using the Support Vector Machine and Maximum Entropy methods. The best results can be obtained using the Maximum Entropy method using all feature extraction with an accuracy of 92.7% and in the Support Vector Machine method, the accuracy obtained is 89.2%.
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References
C. Paper and N. Inspired, “Online Social Media-based Sentiment Analysis for US Airline companies Online Social Media-based Sentiment Analysis for US Airline companies,” no. April, 2017.
A. Alarifi, M. Alsaleh, and A. M. Al-Salman, “Twitter turing test: Identifying social machines,” Inf. Sci. (Ny)., vol. 372, pp. 332–346, 2016.
M. Badri, “Komunikasi Pemasaran UMKM Di Era Media Sosial. Corporate and Marketing Communication,” no. January, p. Jakarta : Pusat Studi Komunikasi dan Bisnis Progra, 2011.
D. Bandorski et al., “Contraindications for video capsule endoscopy,” World J. Gastroenterol., vol. 22, no. 45, pp. 9898–9908, 2016.
E. Kouloumpis, T. Wilson, and J. Moore, “Twitter sentiment analysis: The good the bad and the omg!,” Proc. Fifth Int. AAAI Conf. Weblogs Soc. Media (ICWSM 11), pp. 538–541, 2011.
A. Ortigosa, J. M. Martín, and R. M. Carro, “Sentiment analysis in Facebook and its application to e-learning,” Comput. Human Behav., vol. 31, no. 1, pp. 527–541, 2014.
B. Gupta, I. M. Negi, K. Vishwakarma, G. Rawat, P. Badhani, and B. Tech, “Study of Twitter Sentiment Analysis using Machine Learning Algorithms on Python,” Int. J. Comput. Appl., vol. 165, no. 9, pp. 975–8887, 2017.
A. M. Pravina, I. Cholissodin, and P. P. Adikara, “Analisis Sentimen Tentang Opini Maskapai Penerbangan pada Dokumen Twitter Menggunakan Algoritme Support Vector Machine ( SVM ),” J. Pengemb. Teknol. Inf. dan Ilmu Komput. Univ. Brawijaya, vol. 3, no. 3, pp. 2789–2797, 2019.
A. Rachmat and Y. Lukito, “Klasifikasi Sentimen Komentar Politik dari Facebook Page Menggunakan Naive Bayes,” J. Inform. dan Sist. Inf. Univ. Ciputra, vol. 02, no. 02, pp. 26–34, 2016.
P. Baid, “Sentiment Analysis of Movie Reviews using Machine Learning Techniques,” no. December 2017, 2018.
M. Desai and M. A. Mehta, “Techniques for sentiment analysis of Twitter data: A comprehensive survey,” Proceeding - IEEE Int. Conf. Comput. Commun. Autom. ICCCA 2016, no. April 2016, pp. 149–154, 2017.
T. Jain, N. Agrawal, G. Goyal, and N. Aggrawal, “Sarcasm detection of tweets: A comparative study,” 2017 10th Int. Conf. Contemp. Comput. IC3 2017, vol. 2018-Janua, no. August, pp. 1–6, 2018.
M. S. M. Suhaimin, M. H. A. Hijazi, R. Alfred, and F. Coenen, “Natural language processing based features for sarcasm detection: An investigation using bilingual social media texts,” ICIT 2017 - 8th Int. Conf. Inf. Technol. Proc., pp. 703–709, 2017.
P. B. Awachate and V. P. Kshirsagar, “Improved Twitter Sentiment Analysis Using N Gram Feature Selection and Combinations,” Int. J. Adv. Res. Comput. Commun. Eng. ISO, vol. 3297, no. 9, pp. 154–157, 2007.
A. G. Prasad, S. Sanjana, S. M. Bhat, and B. S. Harish, “Sentiment analysis for sarcasm detection on streaming short text data,” 2017 2nd Int. Conf. Knowl. Eng. Appl. ICKEA 2017, vol. 2017-Janua, no. 2009, pp. 1–5, 2017.
C. Musto, G. Semeraro, and M. Polignano, “A comparison of lexicon-based approaches for sentiment analysis of microblog,” CEUR Workshop Proc., vol. 1314, pp. 59–68, 2014.
Z. Tong and H. Zhang, “A Text Mining Research Based on LDA Topic Modelling,” pp. 201–210, 2016.
D. M. Blei, B. B. Edu, A. Y. Ng, A. S. Edu, M. I. Jordan, and J. B. Edu, “technique...Latent Dirichlet Allocation,” J. Mach. Learn. Res., vol. 3, pp. 993–1022, 2003.
J. Jayalekshmi and T. Mathew, “Facial expression recognition and emotion classification system for sentiment analysis,” 2017 Int. Conf. Networks Adv. Comput. Technol. NetACT 2017, no. July, pp. 1–8, 2017.
V. A. Kharde and S. S. Sonawane, “Sentiment Analysis of Twitter Data: A Survey of Techniques,” Int. J. Comput. Appl., vol. 139, no. 11, pp. 975–8887, 2016.
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