Economic Impact due Covid-19 Pandemic: Sentiment Analysis on Twitter Using Naïve Bayes Classifier and Support Vector Machine

Qurrotul Aini - UIN Syarif Hidayatullah, Ciputat Timur, Tangerang Selatan, Indonesia
Raffie Rizky Fauzi - UIN Syarif Hidayatullah, Ciputat Timur, Tangerang Selatan, Indonesia
Eva Khudzaeva - UIN Syarif Hidayatullah, Ciputat Timur, Tangerang Selatan, Indonesia


Citation Format:



DOI: http://dx.doi.org/10.30630/joiv.7.3.1474

Abstract


Covid-19 is an outbreak caused by severe acute respiratory syndrome. Covid-19 first appeared in Indonesia on March 2, 2020, with two confirmed cases and increased to 1285 cases in 30 provinces. One of the impacts of the Covid-19 pandemic is on the economic aspect, which has experienced a drastic decline in income. This study aims to classify public opinion to determine the level of public sentiment on the economic impact of the Covid-19 pandemic and to identify parameters that influence the accuracy of the sentiment analysis classification model. The methods used in this current research are Lexicon, Support Vector Machine (SVM), and Naive Bayes Classifier (NBC). First, Lexicon is used for scoring and labeling the preprocessed data. Second, SVM is used to classify the sentiment, then find the best accuracy using linear, radial, polynomial, and sigmoid kernels. Third, NBC is used to classify sentiment as a comparison method. The results indicated that 255 tweet data consisted of 44 positive tweets (17.25%), 46 neutral tweets (18.04%), and 165 negative tweets (64.71%). Therefore, it can be inferred that the economic impact on the Indonesian people due to the Covid-19 pandemic has a high negative sentiment value. In the performance, SVM yielded a better accuracy of 100%, precision, recall, and F-measure are 1. This study proves that selecting the kernel type and applying underfitting can improve the accuracy of SVM. Also, SVM can perform well on a small amount of training data.

Keywords


Sentiment analysis; economic impact; Twitter; Naïve Bayes Classifier; Support Vector Machine

Full Text:

PDF

References


W. Setyobudi, A. Alwi, and I. P. Astuti, “Sentiment analysis on twitter for gojek traveloka liga 1 indonesia,†(in Indonesian), Komputek, vol. 2, no. 1, pp. 56–68, 2018, doi: 10.24269/jkt.v2i1.68.

N. M. S. Hadna, P. I. Santosa, and W. W. Winarno, “Literature study about comparison of methods for sentiment analysis process on twitter,†(in Indonesian), in Proc. Seminar Nasional Teknologi Informasi dan Komunikasi, March, pp. 57–64, 2016.

R. Djalante et al., “Review and analysis of current responses to COVID-19 in Indonesia: Period of January to March 2020,†Progress in Disaster Science, vol. 6, pp. 1-9, 2020, doi: 10.1016/j.pdisas.2020.100091.

G. Golar et al., “The social-economic impact of COVID-19 pandemic: implications for potential forest degradation,†Heliyon, vol. 6, no. 10, pp. 1-10, 2020, doi: 10.1016/j.heliyon.2020.e05354.

Badan Pusat Statistik, Analysis of the Survey Results of Covid-19 Impact on Business Actors (in Indonesian). Catalog Report. Jakarta, Indonesia: BPS RI, Sep. 2020.

Y. Lin, X. Wang, and A. Zhou, “Opinion spam detection,†in Opinion Analysis for Online Reviews. Singapore: World Scientific Publishing, 2016, ch. 7, pp. 79–94.

J. Xu et al., “Citation sentiment analysis in clinical trial papers,†in Proc. AMIA Annual Symposium, Nov. 2016, pp. 1334-1341.

N. T. Romadloni, I. Santoso, and S. Budilaksono, “Comparison of naive bayes, KNN, and decision tree methods to sentiment analysis of commuter line KRL transportation,†(in Indonesian), IKRA-ITH Informatika, vol. 3, no. 2, pp. 1-9, Jul. 2019.

S. Kurniawan et al., “Comparative classification method for analysis of sentiment of political figures in online news media comments,†(in Indonesian), Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi), vol. 3, no. 2, pp. 176–183, 2019, doi: 10.29207/resti.v3i2.935.

A. C. Najib, A. Irsyad, G. A. Qandi, and N. A. Rakhmawati, “Comparison of lexicon-based and SVM methods for ontology-based sentiment analysis in the 2019 indonesian presidential election campaign on twitter,†(in Indonesian), Fountain of Informatics Journal, vol. 4, no. 2, pp. 41-48, Nov. 2019, doi: 10.21111/fij.v4i2.3573.

M. Z. Nafan and A. E. Amalia, “Trends in public response to the indonesian economy based on lexicon-based sentiment analysis,†(in Indonesian), Jurnal Media Informatika Budidarma, vol. 3, no. 4, pp. 268-273, Oct. 2019, doi: 10.30865/mib.v3i4.1283.

K. A. F. A. Samah et al., “The best malaysian airline companies visualization through bilingual twitter sentiment analysis: a machine learning classification,†JOIV: Int. J. Inform. Visualization, vol. 6, no. 1, pp. 130-137, Mar. 2022, doi: 10.30630/joiv.6.1.879.

I. Zukhrufillah, “Symptoms of twitter social media as alternative social media,†(in Indonesian), Al-I’lam: Jurnal Komunikasi Dan Penyiaran Islam, vol. 1, no. 2, pp. 102-109, 2018, doi: 10.31764/jail.v1i2.235.

S. R. I. Rezeki, “Use of social media twitter in organizational communication (case study of DKI jakarta provincial government in handling covid-19),†(in Indonesian), Journal of Islamic and Law Studies, vol. 4, no. 2, pp. 63–78, 2020, doi: 10.18592/jils.v4i2.3804.

M. Kashina, I. D. Lenivtceva, and G. D. Kopanitsa, “Preprocessing of unstructured medical data: The impact of each preprocessing stage on classification,†Procedia Computer Science, vol. 178, pp. 284–290, 2020, doi: 10.1016/j.procs.2020.11.030.

N. Mukhtar, M. A. Khan, and N. Chiragh, “Lexicon-based approach outperforms supervised machine learning approach for Urdu sentiment analysis in multiple domain,†Telematics and Informatics, vol. 35, no. 8, pp. 2173–2183, 2018, doi: 10.1016/j.tele.2018.08.003.

S. Soheily-Khah, P. F. Marteau, and N. Bechet, “Intrusion detection in network systems through hybrid supervised and unsupervised machine learning process: A case study on the iscx dataset,†in Proc. 2018 1st International Conference on Data Intelligence and Security ICDIS, 2018, pp. 219–226.

C. C. Aggarwal and C. Xhai, “A survery of text clustring algorithms. mining text data,†in Mining Text Data. Boston, USA: Springer, 2012. ch. 4, pp. 77–128.

R. Feldman and J. Sanger, The Text Mining Handbook: Advanced Approaches in Analyzing Unstructured Data. Cambridge, UK: Cambridge University Press, 2007.

G. Battineni, N. Chintalapudi, and F. Amenta, “Machine learning in medicine: Performance calculation of dementia prediction by support vector machines (SVM),†Informatics in Medicine Unlocked, vol. 16, pp. 1-8, Jun. 2019, doi: 10.1016/j.imu.2019.100200.

N. A. Utami, W. Maharani, and I. Atastina, “Personality classification of facebook users according to big five personality using SVM (support vector machine) method,†Procedia Computer Science, vol. 179, pp. 177–184, 2021, doi: 10.1016/j.procs.2020.12.023.

I. Santoso, W. Gata, and A. B. Paryanti, “Use of feature selection in the support vector machine algorithm for sentiment analysis of the general election commission,†(in Indonesian), Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi), vol. 3, no. 3, pp. 364–370, Dec. 2019, doi: 10.29207/resti.v3i3.1084.

F. Hilmiyah, “Prediction of student performance using support vector machines for study program manager in higher education (case study),†(in Indonesian), M. Eng. thesis, Institut Teknologi Sepuluh Nopember, Surabaya, Indonesia, Jul. 2017.

V. N. Vapnik, The Nature of Statistical Learning Theory, second ed. Red Bank, USA: Springer, 2000.

J. Xu, Y. Zhang, and D. Miao, “Three-way confusion matrix for classification: A measure driven view,†Information Sciences, vol. 507, pp. 772-794, Jan. 2020, doi: 10.1016/j.ins.2019.06.064.

J. D. Novaković et al., “Evaluation of classification models in machine learning,†Theory and Applications of Mathematics & Computer Science, vol. 7, no. 1, pp. 39-46, 2017.

M. Ali, D-H. Son, S-H. Kang, and S-R. Nam, “An accurate CT saturation classification using a deep learning approach based on unsupervised feature extraction and supervised fine-tuning strategy,“ Energies, vol. 10, no. 11, pp. 1-24, 2017, doi: 10.3390/en10111830.

D. A. Fauziah, A. Maududie, and I. Nuritha, “Classification of political news using the k-nearst neighbor algorithm,†(in Indonesian), BERKALA SAINTEK, vol. 6, no. 2, pp. 106-114, 2018, doi: 10.19184/bst.v6i2.9256.

M. Czajkowski and M. Kretowski, “Decision tree underfitting in mining of gene expression data. An evolutionary multi-test tree approach,†Expert Systems with Applications, vol. 137, pp. 392–404, 2019, doi: 10.1016/j.eswa.2019.07.019.

R. Nooraeni et al., “Twitter data sentiment analysis regarding the issue law draft KPK using support vector machine (SVM),†(in Indonesian), Paradigma –Jurnal Informatika dan Komputer, vol. 22, no. 1, pp. 55-60, Mar. 2020, doi: 10.31294/p.v21i2.

K. I. Ruslim, P. P. Adikara, and I. Indriati, “Sentiment analysis on mobile banking application reviews using the support vector machine and lexicon based features methods,†(in Indonesian), Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer, vol. 3, no. 7, pp. 6694–6702, 2019.