Sentiment analysis using fuzzy naïve bayes classifier on covid-19
DOI:
https://doi.org/10.24042/djm.v4i2.7390Keywords:
covid-19, Fuzzy Membership function, Fuzzy Naive Bayes Classiifier, Sentiment AnalysisAbstract
Fuzzy Naive Bayes Classifier method has been widely applied for classification. The Fuzzy Naive Bayes method which consists of a combination of two methods including fuzzy logic and Naive Bayes is used to create a new system that is expected to be better. This research aim to find out the society's sentiments about COVID-19 in Indonesia and the use of the results of the Fuzzy Naive Bayes Classifier. The data of this research is obtained by scraping on Twitter in the period from January 1, 2020 to April 30, 2020. The classification method used in this research is the Fuzzy Naive Bayes Classifier method by applying the fuzzy membership function. In this research, sentiment analysis uses input data whose source is taken from tweets and the output data consists of sentiment data which is classified into three classes, namely positive class, negative class, and neutral class. In the distribution of training and testing data of 70%: 30%, the accuracy of the classification model using the confusion matrix is 83.1% based on 1199 tweet data consisting of 360 testing data and 839 training data. Also the presentation of each sentiment class was obtained which was dominated by positive sentiments, namely the positive class by 36.7%, the negative class by 35.0%, and the neutral class by 28.3%. Based on the results of the presentation, it can be concluded that there are still many people who have positive opinions or give positive responses to the presence of COVID-19 in Indonesia.
References
Agarwal, A., Xie, B., Vovsha, I., Rambow, O., & Passonneau, R. (2011). Sentiment analysis of twitter data. Department of Computer Science. Columbia University.
Balahur, A. (2013). Sentiment analysis in social media texts. European Commission Joint Research Centre.
Buani, D. C. P. (2016). Optimasi algoritma naïve bayes dengan menggunakan algoritma genetika untuk prediksi kesuburan (fertility). Program Studi Teknik Informatika, STMIK Nusa Mandiri Jakarta, 4(1).
Fathurochman, D., Witanti, W., & Yuniarti, R. (2014). Perancangan game turn based strategy menggunakan logika fuzzy dan naive bayes classifier. Conference: Seminar Nasional Informatika At: Yogyakarta, 1(1).
Li, Q., Guan, X., Wu, P., Xiaoye, W., & et al. (2020). Early transmission dynamics in wuhan, china, of novel coronavirus-infected pneumonia. N Engl J Med [Epub Ahead of Print 29 Jan 2020] in Press. https://doi.org/10.1056/NEJMoa2001316
Liu, B. (2012). Sentiment analysis and opinion mining: Synthesis lectures on human language technologies. Morgan and Claypool Publishers. https://doi.org/https://doi.org/10.2200/S00416ED1V01Y201204HLT016
Mahase, E. (2020). China coronavirus: WHO declares international emergency as death toll exceeds 200. BMJ.368:M408. https://doi.org/10.1136/bmj.m408
Manliguez, C. (2016). Generalized confusion matrix for multiple classes. Cinmayii Manliguez. University of the Philippines. https://doi.org/10.13140/RG.2.2.31150.51523
Nasrullah, R. (2015). Media sosial; persfektif komunikasi, budaya, dan sosioteknologi. Simbiosa Rekatama Media.
Nugroho, Didik, G., & Yulison Herry Chrisnanto, A. W. (2016). Analisis sentimen pada jasa ojek online menggunakan metode naïve bayes. Program Studi Informatika, Fakultas Matematika Dan Ilmu Pengetahuan Alam, Universitas Jenderal Achmad Yani, Semarang.
Phelan, A. L., Katz, R., & Gostin, L. O. (2020). The novel coronavirus originating in wuhan, china. China: Challenges for Global Health Governance [Epub Ahead of Print 30 Jan 2020] in Press. JAMA. https://doi.org/10.1001/jama.2020.1097
Poria, S., Cambria, E., & Gelbukh, A. (2016). Aspect extraction for opinion mining with a deep convolutional neural network. Knowledge Based Systems. 42–49.
Slamet, C., Andrian, R., Maylawati, D. S., Suhendar, Darmalaksana, W., & Ramadhani, M. A. (2018). Web scraping and naive bayes classification for job search engine. IOP Conf. Ser.: Mater. Sci. Eng. 288 012038. https://doi.org/10.1088/1757-899X/288/1/012038
Zaidi, N. A., Cerquides, J., Carman, M. J., & Webb, G. I. (2013). Alleviating naive bayes attribute independence assumption by attribute weighting. Journal of Machine Learning Research, 14, 1947–1988.
Zimmerman, H. J. (1996). Fuzzy sets theory and its applications. Massachusetts: Kluwer Academic Publishers.
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