Implementation of IndoBERT for Sentiment Analysis of Indonesian Presidential Candidates

Authors

  • Primanda Sayarizki Telkom University
  • Hasmawati Telkom University
  • Hani Nurrahmi Telkom University

DOI:

https://doi.org/10.34818/INDOJC.2024.9.2.934

Keywords:

Sentiment Analysis, IndoBERT, Deep Learning, Twitter, Public Opinion, Classification Model

Abstract

In this modern era, Indonesian society widely utilizes social media, particularly Twitter, as a means to express their opinions. Every day, various opinions of Indonesian citizens are disseminated on this platform, including their views on prospective presidential candidates for the year 2024. Analyzing public opinions regarding prospective presidential candidates in 2024 is crucial to understanding the sentiment of the people toward these candidates. Such sentiment analysis can be conducted using deep learning techniques such as IndoBERT to acquire knowledge regarding the classification of sentiments as positive, neutral, or negative. IndoBERT is employed to generate vector representations that encapsulate the meaning of tokens, words, phrases, or texts. These representation vectors can then be input into a classification model to perform sentiment analysis. The sentiment classification model undergoes testing with a diverse set of tweets in the test dataset, which represent a wide range of public opinions. The evaluation results indicate an overall accuracy rate of 80%, with precision rates of 62% for negative sentiment, 81% for neutral sentiment, and 85% for positive sentiment. Additionally, the recall rates for each sentiment are 64% for negative, 81% for neutral, and 84% for positive, with corresponding F1-scores of 63%, 81%, and 85%, respectively.

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References

[1] Endrik, A. Nugroho and A. T. Zy, "Penerapan Algoritma Naive Bayes dan PSO pada Analisis Sentimen Kandidat Calon Presiden 2024," vol. 7, no. 3, August 2023.
[2] L. Geni, E. Yulianti and D. S. Indra, "Sentiment Analysis of Tweets Before the 2024 Elections in Indonesia Using Bert Language Models," Jurnal Ilmiah Teknik Elektro Komputer dan Informatika, vol. 9, no. 3, 2023.
[3] S. Imron, E. I. Setiawan and J. Santoso, "Deteksi Aspek Review E-Commerce Menggunakan IndoBERT Embedding dan CNN," INSYST: Journal of Intelligent System and Computation , vol. 5, no. 1, 2023.
[4] C. N. Zempi, A. Kuswanti and S. Maryam, ANALISIS PERAN MEDIA SOSIAL DALAM PEMBENTUKAN PENGETAHUAN POLITIK MASYARAKAT, vol. 6, Jurnal Ilmu Komunikasi, 2023.
[5] H. Jayadianti, W. Kaswidjanti, A. T. Utomo, F. A. Dwiyanto and R. Drezewski, "Sentiment analysis of Indonesian reviews using fine-tuning IndoBERT and R-CNN," Ilkom Jurnal Ilmiah, vol. 14, no. 3, pp. 348-354, 2022.
[6] B. Wilie, K. Vincentio, G. I. Winata, S. Cahyawijaya, X. Li, Z. Y. Lim, S. Soleman, R. Mahendra, P. Fung, S. Bahar and A. Purwarianti, "IndoNLU: Benchmark and Resources for Evaluating Indonesian Natural Language Understanding," in 1st Conf. Asia-Pacific Chapter Assoc. Comput. Linguist., 2020.
[7] K. S. Nugroho, A. Y. Sukmadewa, H. W. DW, F. A. Bachtiar and N. Yudistira, "BERT Fine-Tuning for Sentiment Analysis on Indonesian Mobile Apps Reviews," in SIET '21: 6th International Conference on Sustainable Information Engineering and Technology, 2021.
[8] A. L. Hananto, A. P. Nardilasari, A. Fauzi, A. Hananto, B. Priyatna and A. Y. Rahman, "Best Algorithm in Sentiment Analysis of Presidential Election in Indonesia on Twitter Authors," International Journal of Intelligent Systems and Applications in Engineering, vol. 11, no. 6s, p. 473–481, 2023.
[9] M. Rizki, M. F. Hidayattullah and D. I. Af'idah, "Klasifikasi Opini Publik di Twitter Terhadap Bakal Calon Presiden Indonesia Tahun 2024 Menggunakan LSTM Secara Realtime Berbasis Website," INFOTEKMESIN, vol. 14, no. 2, p. 285–295, 2023.
[10] S. Saadah, K. M. Auditama, A. A. Fattahila, F. I. Amorokhman, A. Aditsania and A. A. Rohmawati, "Implementation of BERT, IndoBERT, and CNN-LSTM in Classifying Public Opinion about COVID-19 Vaccine in Indonesia," JURNAL RESTI(Rekayasa Sistem dan Teknologi Informasi), vol. 6, no. 4, pp. 648-655, 2022.
[11] N. K. Nissa and E. Yulianti, "Multi-label text classification of Indonesian customer reviews using bidirectional encoder representations from transformers language model," International Journal of Electrical and Computer Engineering, vol. 13, no. 5, pp. 5641-5652, 2023.
[12] H. Zhu, "Sentiment analysis of 2021 Canadian election tweets," in International Conference on Artificial Intelligence, Virtual Reality, and Visualization (AIVRV 2022), Chongqing, 2023.
[13] J. Yadav, "Sentiment Analysis on Social Media," 9 January 2023.
[14] M. Wankhade, A. C. S. Rao and C. Kulkarni, "A survey on sentiment analysis methods, applications, and challenges," Artificial Intelligence Review, vol. 55, no. 7, p. 5731–5780, 2022.
[15] D. Rothman, Transformers for Natural Language Processing, Birmingham,Mumbai: Packt Publishing Ltd, 2021.
[16] L. F. Simanjuntak, R. Mahendra and E. Yulianti, "We Know You Are Living in Bali: Location Prediction of Twitter Users Using BERT Language Model," Big Data and Cognitive Computing , vol. 6, no. 3, 2022.
[17] F. Koto, A. Rahimi, J. H. Lau and T. Baldwin, "IndoLEM and IndoBERT: A Benchmark Dataset and Pre-trained Language Model for Indonesian NLP," in The 28th International Conference on Computational Linguistics, 2020.
[18] A. Awalina, F. A. Bachtiar and F. Utaminingrum, "Perbandingan Pretrained Model Transformer pada Deteksi Ulasan Palsu," Jurnal Teknologi Informasi dan Ilmu Komputer, vol. 9, no. 3, pp. 597-604, 2022.
[19] J. Devlin, M.-W. Chang, K. Lee and K. Toutanova, "BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding," 2019.

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Published

2024-08-30

How to Cite

Primanda Sayarizki, Hasmawati, & Nurrahmi, H. (2024). Implementation of IndoBERT for Sentiment Analysis of Indonesian Presidential Candidates. Indonesian Journal on Computing (Indo-JC), 9(2), 61–72. https://doi.org/10.34818/INDOJC.2024.9.2.934

Issue

Vol. 9 No. 2 (2024): August, 2024

Section

Computer Science

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