Analysis of twitter user sentiment on the monkeypox virus issue using the nrc lexicon
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Published:
Feb 28, 2023
Keywords:
Monkeypox, NRC Lexicon, Sentiment analysis, Text mining, RStudio
Section
Computer Science
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Abstract
Monkeypox virus is closely related to variola (smallpox virus) and causes a smallpox-like disease. The increase in monkeypox cases has caused the general public to be involved in providing responses to seek and share information related to monkeypox on the internet, especially on social media platforms. This study aims to analyze a collection of 5000 tweets on August 5, 2022, for sentiment analysis using the NRC lexicon. Of the 5,000 tweets that have been extracted, it is obtained that the words that Twitter users often use are "health", "emergency", "public", "covid", and "declares". By using the classification using the NRC lexicon comparison, we found that the emotion type of fear was the most widely used emotion, which had a presentation of 19.73%, followed by anticipation emotion at 16.78%, sadness 14.77%, trust at 13.90%, anger 9.99%, shock 9.14%, disgust 8.12%, and happy 1288 7.90 %. The negative sentiment that often appears on Twitter is equal to 51.92%, and positive sentiment has a percentage of 48.08%. The negative sentiment words that appear most often are "emergency", "virus", "disease", "shit", and "risk". The positive sentiment words that appear most often are "public", "vaccine", "sex", "contact", and "united". The analytical method with the lexicon method is very well used in analyzing various emotions and sentiments on social media. %MCEPASTEBIN%
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How to Cite
Zuhanda, M. K., Syofra, A. H. S., Mathelinea, D., Gio, P. U. ., Anisa, Y. A. and Novita, N. . (2023) “Analysis of twitter user sentiment on the monkeypox virus issue using the nrc lexicon ”, Jurnal Mantik, 6(4), pp. 3854-3860. doi: 10.35335/mantik.v6i4.3502.
References
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Zuhanda, M. K., Desniarti, & Hafiz, M. (2022). Twitter User Sentiment Analysis on the Mysterious Hepatitis Issue. JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING, 6(1), 258–266. https://doi.org/10.31289/jite.v6i1.7326
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Berthet, N., Descorps-Declère, S., Besombes, C., Curaudeau, M., Nkili Meyong, A. A., Selekon, B., Labouba, I., Gonofio, E. C., Ouilibona, R. S., Simo Tchetgna, H. D., Feher, M., Fontanet, A., Kazanji, M., Manuguerra, J. C., Hassanin, A., Gessain, A., & Nakoune, E. (2021). Genomic history of human monkey pox infections in the Central African Republic between 2001 and 2018. Scientific Reports, 11(1). https://doi.org/10.1038/s41598-021-92315-8
Dell’Amico, M., Montemanni, R., & Novellani, S. (2020). Matheuristic algorithms for the parallel drone scheduling traveling salesman problem. Annals of Operations Research, 289(2). https://doi.org/10.1007/s10479-020-03562-3
Duncombe, C. (2019). The Politics of Twitter: Emotions and the Power of Social Media. International Political Sociology, 13(4). https://doi.org/10.1093/ips/olz013
Ferra, I., & Nguyen, D. (2017). #Migrantcrisis: “tagging” the European migration crisis on Twitter. Journal of Communication Management, 21(4). https://doi.org/10.1108/JCOM-02-2017-0026
Garcia, K., & Berton, L. (2021). Topic detection and sentiment analysis in Twitter content related to COVID-19 from Brazil and the USA. Applied Soft Computing, 101. https://doi.org/10.1016/j.asoc.2020.107057
Grover, A., Kumar, P., & Garg, K. (2020). Improvisation of Cleaning Process on Tweets for Opinion Mining. International Journal of Big Data and Analytics in Healthcare, 5(1). https://doi.org/10.4018/ijbdah.2020010104
Guagliardo, S. A. J., Monroe, B., Moundjoa, C., Athanase, A., Okpu, G., Burgado, J., Townsend, M. B., Satheshkumar, P. S., Epperson, S., Doty, J. B., Reynolds, M. G., Dibongue, E., Etoundi, G. A., Mathieu, E., & McCollum, A. M. (2020). Asymptomatic orthopoxvirus circulation in humans in the wake of a monkeypox outbreak among chimpanzees in Cameroon. American Journal of Tropical Medicine and Hygiene, 102(1). https://doi.org/10.4269/ajtmh.19-0467
Harapan, H., Wagner, A. L., Yufika, A., Setiawan, A. M., Anwar, S., Wahyuni, S., Asrizal, F. W., Sufri, M. R., Putra, R. P., Wijayanti, N. P., Salwiyadi, S., Maulana, R., Khusna, A., Nusrina, I., Shidiq, M., Fitriani, D., Muharrir, M., Husna, C. A., Yusri, F., … Mudatsir, M. (2020). Acceptance and willingness to pay for a hypothetical vaccine against monkeypox viral infection among frontline physicians: A cross-sectional study in Indonesia. Vaccine, 38(43). https://doi.org/10.1016/j.vaccine.2020.08.034
Hassan, M. K., Hudaefi, F. A., & Caraka, R. E. (2022). Mining netizen’s opinion on cryptocurrency: sentiment analysis of Twitter data. Studies in Economics and Finance, 39(3), 365–385. https://doi.org/10.1108/SEF-06-2021-0237
Heskin, J. et al. (2022). Transmission of monkeypox virus through sexual contact - A novel route of infection. Journal Infection, 85(3), 334–363.
Hooper, J. W., Thompson, E., Wilhelmsen, C., Zimmerman, M., Ichou, M. A., Steffen, S. E., Schmaljohn, C. S., Schmaljohn, A. L., & Jahrling, P. B. (2004). Smallpox DNA Vaccine Protects Nonhuman Primates against Lethal Monkeypox. Journal of Virology, 78(9). https://doi.org/10.1128/jvi.78.9.4433-4443.2004
Ismail, S. A., Saliba, V., Lopez Bernal, J., Ramsay, M. E., & Ladhani, S. N. (2021). SARS-CoV-2 infection and transmission in educational settings: a prospective, cross-sectional analysis of infection clusters and outbreaks in England. The Lancet Infectious Diseases, 21(3). https://doi.org/10.1016/S1473-3099(20)30882-3
Kausar, M. A., Soosaimanickam, A., & Nasar, M. (2021). Public Sentiment Analysis on Twitter Data during COVID-19 Outbreak. International Journal of Advanced Computer Science and Applications, 12(2). https://doi.org/10.14569/IJACSA.2021.0120252
Kesavarapu, K., Zelenkauskaite, A., & Nandi, N. (2021). Reach and Power of Physician-Initiated Tweets in a Twitter Inflammatory Bowel Disease Community. Crohn’s and Colitis 360, 3(3). https://doi.org/10.1093/crocol/otab052
Liu, Y., Wert, G., Greenawald, B., Boni, M. al, & Brown, D. E. (2018). Predicting violent behavior using language agnostic models. IC3K 2018 - Proceedings of the 10th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management, 1. https://doi.org/10.5220/0006933701020109
Mahase, E. (2022). Monkeypox: What do we know about the outbreaks in Europe and North America? The BMJ.
Mathur, A., Kubde, P., & Vaidya, S. (2020a). Emotional analysis using twitter data during pandemic situation: Covid-19. Proceedings of the 5th International Conference on Communication and Electronics Systems, ICCES 2020. https://doi.org/10.1109/ICCES48766.2020.09138079
Mathur, A., Kubde, P., & Vaidya, S. (2020b). Emotional Analysis using Twitter Data during Pandemic Situation: COVID-19. https://doi.org/10.1109/icces48766.2020.9138079
Mbala, P. K., Huggins, J. W., Riu-Rovira, T., Ahuka, S. M., Mulembakani, P., Rimoin, A. W., Martin, J. W., & Muyembe, J. J. T. (2017). Maternal and Fetal Outcomes among Pregnant Women with Human Monkeypox Infection in the Democratic Republic of Congo. Journal of Infectious Diseases, 216(7). https://doi.org/10.1093/infdis/jix260
Pérez-Dasilva, J. Á., Meso-Ayerdi, K., & Mendiguren-Galdospín, T. (2020). Fake news and coronavirus: Detecting key players and trends through analysis of twitter conversations. Profesional de La Informacion, 29(3). https://doi.org/10.3145/epi.2020.may.08
Plotly. (2019). Plotly: Modern Analytic Apps for the Enterprise - Plotly. Plotly.
Thakur, N. (2022). MonkeyPox2022Tweets: A Large-Scale Twitter Dataset on the 2022 Monkeypox Outbreak, Findings from Analysis of Tweets, and Open Research Questions. Infectious Disease Reports, 14(6), 855–883. https://doi.org/10.3390/idr14060087
Tyner, S., Briatte, F., & Hofmann, H. (2017). Network visualization with ggplot2. R Journal, 9(1). https://doi.org/10.32614/rj-2017-023
Verma, P., Khanday, A. M. U. D., Rabani, S. T., Mir, M. H., & Jamwal, S. (2019). Twitter sentiment analysis on Indian government project using R. International Journal of Recent Technology and Engineering, 8(3), 8338–8341. https://doi.org/10.35940/ijrte.C6612.098319
Zad, S., Jimenez, J., & Finlayson, M. (2021). Hell Hath No Fury? Correcting Bias in the NRC Emotion Lexicon. https://doi.org/10.18653/v1/2021.woah-1.11
Zuhanda, M. K., Anisa, Y., Desniarti, D., Hafiz, M., Syofra, A. H., Caraka, R. E., & Noh, M. (2023). Supply chain strategy during the COVID-19 terms: sentiment analysis and knowledge discovery through text mining. Indonesian Journal of Electrical Engineering and Computer Science, 30(2), 1120. https://doi.org/10.11591/ijeecs.v30.i2.pp1120-1127
Zuhanda, M. K., Desniarti, & Hafiz, M. (2022). Twitter User Sentiment Analysis on the Mysterious Hepatitis Issue. JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING, 6(1), 258–266. https://doi.org/10.31289/jite.v6i1.7326
Zuhanda, M. K., Suwilo, S., Sitompul, O. S., Mardiningsih, Caraka, R. E., Kim, Y., & Noh, M. (2023). Optimization of Vehicle Routing Problem in the Context of E-commerce Logistics Distribution.
Zuhanda, M. K., Suwilo, S., Sitompul, O. S., & Mardiningsih, M. (2022). A combination k-means clustering and 2-opt algorithm for solving the two echelon e-commerce logistic distribution. Logforum, 18(2), 213–225. https://doi.org/10.17270/J.LOG.2022.734
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