Analisis Sentimen Twitter Terhadap Opini Publik Atas Isu Pencalonan Puan Maharani dalam PILPRES 2024
Abstract
Twitter can be seen as a platform for candidates and users to gain substantial reach to show their views on who the president will be elected to in 2024. The aim of this study is to explore contrasting information over time regarding whether Puan Maharani can be one of the candidates. The best according to the Indonesian people. In this study, sentiment analysis was carried out using the text mining method and several libraries such as TextBlob, VaderSentiment, and SentiWordNet to retrieve and classify the polarity of opinions from data that had been crawled. In the dataset generated with the keyword “Puan Maharani” The average negative sentiment is only 0.1%, neutral sentiment is 97.25, and positive sentiment is 2.55%. It can be concluded that Twitter users tend to be neither aggressive nor defensive in discussing issues leading to the candidacy of Puan Maharani in the upcoming 2024 Indonesian presidential election.
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References
Q. D. Latansa, K. B. M. J. Presiden, and W. P. di Indonesia, ‘Jurist-Diction: Vol. 2 No. 2’. Maret, 2019.
R. Ardiansyah, ‘Analisis sentimen calon presiden dan wakil presiden periode 2019-2024 pasca debat pilpres di Twitter’, Sci. Comput. Sci. Informatics J., vol. 2, no. 1, pp. 21–28, 2019.
G. Aletti, I. Crimaldi, and F. Saracco, ‘A model for the Twitter sentiment curve’, PLoS One, vol. 16, no. 4, pp. 1–28, 2021.
Nurfaizah, I. D. Prawitasari, and Fathuzaen, ‘Analisis Sentimen Twitter Terhadap Opini Publik Pemilihan Kepala Daerah’, in CITISEE, 2018, pp. 299–303.
B. P. Pokharel, ‘Twitter sentiment analysis during covid-19 outbreak in nepal’, Available SSRN 3624719, 2020.
S. S. Singh, B. Dwarakanath, S. Santhoshini, and J. Mary, ‘Twitter sentimental analysis’, J. Adv. Res. Dyn. Control Syst., vol. 12, no. 5 Special Issue, pp. 868–873, 2020.
I. G. S. Mas Diyasa, N. M. I. Marini Mandenni, M. I. Fachrurrozi, S. I. Pradika, K. R. Nur Manab, and N. R. Sasmita, ‘Twitter Sentiment Analysis as an Evaluation and Service Base On Python Textblob’, IOP Conf. Ser. Mater. Sci. Eng., vol. 1125, no. 1, p. 012034, 2021.
K. M. Azharul Hasan, S. Das Shovon, N. H. Joy, and M. S. Islam, ‘Automatic Labeling of Twitter Data for Developing COVID-19 Sentiment Dataset’, in 2021 5th International Conference on Electrical Information and Communication Technology, EICT 2021, 2021, no. February.
S. Ramesh, ‘Sentimental Analysis for Political Polarization Using VADER Sentiment Lexicon’, J. Xidian Univ., vol. 15, no. 5, 2021.
E. Y. Hidayat, R. W. Hardiansyah, and A. Affandy, ‘Analisis Sentimen Twitter untuk Menilai Opini Terhadap Perusahaan Publik Menggunakan Algoritma Deep Neural Network’, J. Nas. Teknol. dan Sist. Inf., vol. 7, no. 2, pp. 108–118, 2021.
T. A. M. Putri, U. Enri, and B. N. Sari, ‘Analisis Algoritma Naive Bayes Classifier untuk Klasifikasi Tweet Pelecehan Seksual dengan# MeToo’, IJCIT (Indonesian J. Comput. Inf. Technol., vol. 5, no. 2, 2020.
M. I. Khatami and others, ‘Eksistensi “Baliho 2024” dalam Pertarungan Elektabilitas: Analisis Pencitraan Tokoh Politik’, J. Ilmu Sos. dan Ilmu Polit. Univ. Jambi, vol. 5, no. 2, pp. 14–24, 2021.
G. A. Buntoro, ‘Analisis Sentimen Calon Gubernur Jawa Timur 2018 di Twitter’, Sci. Comput. Sci. Informatics J., vol. 1, no. 2, pp. 37–45, 2018.
E. De Haan and E. Menichelli, ‘The incremental value of unstructured data in predicting customer churn’, MSI Work. Pap. Ser., vol. 20, no. 105, pp. 1–49, 2020.
X. Zhang, J. L. Ram’irez-Mendiola, M. Li, and L. Guo, ‘Electricity consumption pattern analysis beyond traditional clustering methods: A novel self-adapting semi-supervised clustering method and application case study’, Appl. Energy, vol. 308, p. 118335, 2022.
D. E. Kurniawan, and A. Fatulloh, ‘Clustering of Social Conditions in Batam, Indonesia Using K-Means Algorithm and Geographic Information System’, Int. J. Earth Sci. Eng., vol. 10, no. 05, pp. 1076–1080, 2017.
H. Bhoir and K. Jayamalini, ‘Implementation of Visual Sentiment Analysis on Flickr Images’, 2021.
D. Suhartono, D. Christiandy, and R. Rolando, ‘Lemmatization Technique in Bahasa: Indonesian Language’, J. Softw., vol. 9, no. 5, 2014.
S. Christina and D. Ronaldo, ‘A Survey Of Sentiment Analysis Using Sentiwordnet On Bahasa Indonesia’, J. Teknol. Inf. J. Keilmuan dan Apl. Bid. Tek. Inform., vol. 12, no. 2, pp. 69–73, 2018.
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