Comparison ff Sentiment Labeling Using Textblob, Vader, and Flair in Public Opinion Analysis Post-2024 Presidential Inauguration with IndoBERT
DOI:
https://doi.org/10.52436/1.jutif.2025.6.2.4015Keywords:
Flair, IndoBERT, Presidential Election, Sentimen Analysis, Textblob, VaderAbstract
The results of the 2024 Indonesian presidential election decided that Prabowo Subianto and Gibran Rakabuming Raka became the elected pair of Indonesian presidential and vice-presidential candidates in 2024. The pair's election triggered various public reactions, especially on social media platforms. Some social media platforms provided diverse opinions, indicating a wide variety of views on this issue. This research aims to analyze public opinion after the election of the 2024 Indonesian president by comparing sentiment using TextBlob, VADER (Valence Aware Dictionary and sEntiment Reasoner), and Flair. Training and testing are done with the IndoBERT model to determine the most effective sentiment labeling. This research starts by collecting text data from social media X, YouTube, and Instagram, then preprocessing, translating, and labeling data using three libraries, training, and testing using IndoBERT. The results of training and testing data show that Flair has an accuracy of 81.29%, TextBlob has an accuracy of 73.35%, and VADER has an accuracy of 74.86%. From the accuracy results obtained, it can be concluded that labeling using Flair provides the greatest accuracy of the others because the Flair labeling process uses deep learning and contextual embedding techniques.
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References
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[22] A. Muzaki and A. Witanti, “SENTIMENT ANALYSIS OF THE COMMUNITY IN THE TWITTER TO THE 2020 ELECTION IN PANDEMIC COVID-19 BY METHOD NAIVE BAYES CLASSIFIER SENTIMEN ANALISIS MASYARAKAT DI TWITTER TERHADAP PILKADA 2020 DITENGAH PANDEMIC COVID-19 DENGAN METODE NA Í VE BAYES CLASSIFIER,” J. Tek. Inform., vol. 2, no. 2, pp. 101–107, 2021, doi: 10.20884/1.jutif.2021.2.2.51.
[23] A. C. Khotimah et al., “COMPARISON NAÏVE BAYES CLASSIFIER , K-NEAREST NEIGHBOR AND SUPPORT VECTOR MACHINE IN THE CLASSIFICATION OF INDIVIDUAL ON PERBANDINGAN ALGORITMA NAÏVE BAYES CLASSIFIER , K-NEAREST NEIGHBOR DAN SUPPORT VECTOR MACHINE DALAM KLASIFIKASI,” J. Tek. Inform., vol. 3, no. 3, pp. 673–680, 2022, doi: DOI: https://doi.org/10.20884/1.jutif.2022.3.3.254.
[24] R. Puspitasari, Y. Findawati, M. A. Rosid, I. S. Program, and U. M. Sidoarjo, “SENTIMENT ANALYSIS OF POST-COVID-19 INFLATION BASED ON TWITTER USING THE K-NEAREST NEIGHBOR AND SUPPORT VECTOR MACHINE ANALISIS SENTIMEN TERHADAP INFLASI PASCA COVID-19 BERDASARKAN TWITTER DENGAN METODE KLASIFIKASI K-NEAREST NEIGHBOR DAN,” J. Tek. Inform., vol. 4, no. 4, pp. 669–679, 2023, doi: https://doi.org/10.52436/1.jutif.2023.4.4.801.
[25] R. Sistem, I. M. S. Putra, P. Jhonarendra, N. Kadek, and D. Rusjayanthi, “Deteksi Kesamaan Teks Jawaban pada Sistem Test Essay Online dengan Pendekatan Neural Network,” J. RESTI (Rekayasa Sist. Dan Teknol. Inf., vol. 5, no. 158, pp. 3–12, 2021, doi: https://doi.org/10.29207/resti.v5i6.3544.
[26] K. Makkar, P. Kumar, M. Poriye, and S. Aggarwal, “Improving Sentiment Analysis using Negation Scope Detection and Negation Handling,” Int. J. Comput. Digit. Syst., vol. 1, no. 1, pp. 239–247, 2024, doi: http://dx.doi.org/10.12785/ijcds/160119.
[27] D. Hazarika, G. Konwar, and S. Deb, “Sentiment Analysis on Twitter by Using TextBlob for Natural Language Processing,” Proc. Int. Conf. Res. Manag. Technovation, vol. 24, pp. 63–67, 2020, doi: 10.15439/2020KM20.
[28] S. Dewi and D. B. Arianto, “TWITTER SENTIMENT ANALYSIS TOWARDS QATAR AS HOST OF THE 2022 WORLD CUP USING TEXTBLOB,” J. Soc. Res., no. 2018, pp. 443–454, 2022, doi: https://doi.org/10.55324/josr.v2i2.615.
[29] M. Arief and N. A. Samsudin, “Hybrid Approach with VADER and Multinomial Logistic Regression for Multiclass Sentiment Analysis in Online Customer Review,” Int. J. Adv. Comput. Sci. Appl., vol. 14, no. 12, pp. 311–320, 2023, doi: https://doi.org/10.3390/s23010506.
[30] T. Pano and R. Kashef, “A Complete VADER-Based Sentiment Analysis of Bitcoin ( BTC ) Tweets during the Era of COVID-19,” 2020, doi: https://doi.org/10.3390/bdcc4040033.
[31] G. B. Herwanto, A. M. Ningtyas, I. G. Mujiyatna, and I. Nyoman, “Hate Speech Detection in Indonesian Twitter using Contextual Embedding Approach,” IJCCS (Indonesian J. Comput. Cybern. Syst., vol. 15, no. 2, 2021, doi: 10.22146/ijccs.64916.
[32] M. F. Ali, R. Irfan, and T. A. Lashari, “Comprehensive sentimental analysis of tweets towards COVID-19 in Pakistan : a study on governmental preventive measures,” PeerJ Comput. Sci., 2023, doi: 10.7717/peerj-cs.1220.
[33] A. Fadlil, I. Riadi, and F. Andrianto, “Improving Sentiment Analysis in Digital Marketplaces through SVM Kernel Fine-Tuning,” Int. J. Comput. Digit. Syst., vol. 1, no. 1, 2024, doi: http://dx.doi.org/10.12785/ijcds/160113.
[34] E. Yulianti and N. K. Nissa, “ABSA of Indonesian customer reviews using IndoBERT : single- sentence and sentence-pair classification approaches,” vol. 13, no. 5, pp. 3579–3589, 2024, doi: 10.11591/eei.v13i5.8032.
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2025-04-26
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K. Kusnawi and K. . Anam, “Comparison ff Sentiment Labeling Using Textblob, Vader, and Flair in Public Opinion Analysis Post-2024 Presidential Inauguration with IndoBERT”, J. Tek. Inform. (JUTIF), vol. 6, no. 2, pp. 803–818, Apr. 2025.
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