SVM Method with FastText Representation Feature for Classification of Twitter Sentiments Regarding the Covid-19 Vaccination Program

Mukti M Kusairi; Surya Agustian

doi:10.31849/digitalzone.v13i2.11531

Mukti M Kusairi Universitas Islam Negeri Sultan Syarif Kasim Riau
Surya Agustian Universitas Islam Negeri Sultan Syarif Kasim Riau

DOI: https://doi.org/10.31849/digitalzone.v13i2.11531

Keywords: klasifikasi sentimen, FastText, SVM, vaksin Covid-19

Abstract

Covid-19 is a virus that has a high level of spread, making the government implement a mass vaccination program throughout Indonesia. This program received a lot of responses from the public, with positive and negative opinions or comments. Currently, the public's response through social media is also an input and consideration for the government to implement a program. Therefore, this study was conducted to produce a method approach to assessing the Covid-19 vaccination program by calculating the percentage of each sentiment class. The method used is the Support Vector Machine (SVM) and the fasttext language model feature as a representation of words in the Covid-19 vaccination sentiment dataset collected from Twitter. The data used has been dataset balancing, feature selection and parameter tuning, the optimal SVM model is obtained with a composition of 2536 training data, 778 development data and testing of 400 testing data, resulting in the best value of fi-1 score of 59% with an accuracy rate of 68%. The system is quite successful in detecting sentiment in tweets compared to before.
Keywords: sentiment classification, FastText, SVM, Covid-19 vaccine.

Downloads

Download data is not yet available.

References

M. Rizky, Vaksin Covid-19 Menggunakan Metode Support Vector Machine Pada Media Sosial Twitter Covid-19 Menggunakan Metode Support Vector. 2021.

F. F. Rachman and S. Pramana, “Analisis Sentimen Pro dan Kontra Masyarakat Indonesia tentang Vaksin COVID-19 pada Media Sosial Twitter,” Heal. Inf. Manag. J., vol. 8, no. 2, pp. 100–109, 2020, [Online]. Available: https://inohim.esaunggul.ac.id/index.php/INO/article/view/223/175.

D. Inayah and F. L. Purba, “Implementasi Social Network Analysis Dalam Penyebaran Informasi Virus Corona (Covid-19) Di Twitter,” Semin. Nas. Off. Stat., vol. 2020, no. 1, pp. 292–299, 2021, doi: 10.34123/semnasoffstat.v2020i1.573.

N. Yunita, “Analisis Sentimen Berita Artis Dengan Menggunakan Algoritma Support Vector Machine dan Particle Swarm Optimization,” J. Sist. Inf. STMIK Antar Bangsa, vol. 5, no. 2, pp. 104–112, 2016.

D. H. Anto Satriyo Nugroho, Arief Budi Witarto, “Support vector machine teori dan Aplikasinya dalam Bioinformatika,” Mach. Learn., pp. 1–11, 2003.

T. S. Sabrila, V. R. Sari, and A. E. Minarno, “Analisis Sentimen Pada Tweet Tentang Penanganan Covid - 19 Menggunakan Word Embedding Pada Algoritma Support Vector Machine Dan K - Nearest Neighbor,” vol. 6, no. 2, 2021.

M. D. Rhman, A. Djunaidy, and F. Mahananto, “Penerapan Weighted Word Embedding pada Pengklasifikasian Teks Berbasis Recurrent Neural Network untuk Layanan Pengaduan Perusahaan Transportasi,” vol. 10, no. 1, 2021.

E. Lim, T. I. Istts, E. I. Setiawan, and T. I. Istts, “Stance Classification Post Kesehatan di Media Sosial Dengan FastText Embedding dan Deep Learning,” pp. 65–73, 2019.

A. P. Natasuwarna, “Seleksi Fitur Support Vector Machine pada Analisis Sentimen Keberlanjutan Pembelajaran Daring,” Techno.Com, vol. 19, no. 4, pp. 437–448, 2020, doi: 10.33633/tc.v19i4.4044.

S. Hikmawan, A. Pardamean, and S. N. Khasanah, “Sentimen Analisis Publik Terhadap Joko Widodo terhadap wabah Covid-19 menggunakan Metode Machine Learning,” J. Kaji. Ilm., vol. 20, no. 2, pp. 167–176, 2020, doi: 10.31599/jki.v20i2.117.

P. (Universitas I. N. S. S. K. R. Yohana, “Analisis Sentimen Masyarakat Terhadap Kebijakan Pemerintah Indonesia Dalam Memberikan Vaksin Covid-19 Menggunakan Metode Naive Bayes Classifier.” 2022.

M. Ihsan et al., “LSTM (Long Short Term Memory) for Sentiment COVID-19 Vaccine Classification on Twitter 1,2,3,” pp. 79–89, 2022.

M. Sahbuddin and S. Agustian, “JITE ( Journal of Informatics and Telecommunication Engineering ) Support Vector Machine Method with Word2vec for Covid-19 Vaccine Sentiment Classification on Twitter,” vol. 6, no. July, pp. 288–297, 2022.

A. Nurdin, B. Anggo Seno Aji, A. Bustamin, and Z. Abidin, “Perbandingan Kinerja Word Embedding Word2Vec, Glove, Dan Fasttext Pada Klasifikasi Teks,” J. Tekno Kompak, vol. 14, no. 2, p. 74, 2020, doi: 10.33365/jtk.v14i2.732.

A. Rachmat and Y. Lukito, “Implementasi Sistem Crowdsourced Labelling Berbasis Web dengan Metode Weighted Majority Voting,” J. Ultim. InfoSys, vol. 6, no. 2, pp. 76–82, 2016, doi: 10.31937/si.v6i2.223.

A. Aziz, R. Saptono, and K. P. Suryajaya, “Implementasi Vector Space Model dalam Pembangkitan Frequently Asked Questions Otomatis dan Solusi yang Relevan untuk Keluhan Pelanggan,” Sci. J. Informatics, vol. 2, no. 2, p. 111, 2016, doi: 10.15294/sji.v2i2.5076.