Sentiment Analysis Of Comments On Indonesian Political Speech Videos On Youtube Using FastText
Abstract
The advancement of digital technology has transformed how society accesses and responds to political information, particularly through platforms like YouTube, which serve as arenas for public discourse. Comments on political speech videos often contain complex sentiments such as irony, slang, and code-mixing, which are difficult to identify using traditional sentiment analysis methods. This study aims to analyze public sentiment toward the Indonesian President’s political speeches on YouTube from 2014 to 2024 using the FastText word embedding approach and to compare its performance with the TF-IDF + Logistic Regression method. The evaluation was conducted on three sentiment classes using automatically labeled data and oversampling experiments to address class imbalance. The results show that FastText achieved an accuracy of 76.82%, slightly higher than TF-IDF + Logistic Regression at 74.11%. Although the difference in accuracy is relatively small, the FastText model demonstrated more stable performance on informal texts and varied contexts. The use of oversampling helped balance predictions across classes without significantly improving accuracy. This study highlights the potential of FastText to enhance the effectiveness of Indonesian-language sentiment analysis, particularly for political comments on social media, while also revealing the limitations of automatic labeling that may affect classification outcomes.
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References
Agung, P., Wijaya, A., Dika, I. M., Hendra, I. K., & Jaya, T. (2024). Comprehensive Analysis of Teacher Teaching Performance Through Sentiment and POS Tagging. 12(3), 147–156.
Alfariqi, F., Maharani, W., & Husen, J. H. (2020). Klasifikasi Sentimen pada Twitter dalam Membantu Pemilihan Kandidat Karyawan dengan Menggunakan Convolutional Neural Network dan Fasttext Embeddings. E-Proceeding of Engineering, 7(2), 8052–8062.
Aziz, A. (2022). Analisis Sentimen Identifikasi Opini Terhadap Produk, Layanan dan Kebijakan Perusahaan Menggunakan Algoritma TF-IDF dan SentiStrength. Jurnal Sains Komputer & Informatika (J-SAKTI, 6(1), 115.
Bärtl, M. (2018). YouTube channels, uploads and views: A statistical analysis of the past 10 years. Convergence, 24(1). https://doi.org/10.1177/1354856517736979
Bojanowski, P., Grave, E., Joulin, A., & Mikolov, T. (2017a). Enriching Word Vectors with Subword Information. Transactions of the Association for Computational Linguistics, 5, 135–146. https://doi.org/10.1162/tacl_a_00051
Bojanowski, P., Grave, E., Joulin, A., & Mikolov, T. (2017b). Transactions of the Association for Computational Linguistics. Transactions of the Association for Computational Linguistics, 5, 135–146. https://transacl.org/ojs/index.php/tacl/article/view/999
Davidov, D. (2010). C10-2028.pdf. August, 241–249. https://www.aclweb.org/anthology/C10- 2028.pdf
Fasttext, M., Long, D. A. N., Lstm, S. M., Pangestu, A. F., Rahmat, B., & Sihananto, A. N. (2024). Analisis sentimen pada media sosial x terhadap implementasi kurikulum merdeka menggunakan metode fasttext dan long short-term memory (lstm). 9(4), 2271– 2280.
Gunawan, Y., Young, J. C., & Rusli, A. (2022). FastText Word Embedding and Random Forest Classifier for User Feedback Sentiment Classification in Bahasa Indonesia. Ultimatics : Jurnal Teknik Informatika, 13(2), 101–107. https://doi.org/10.31937/ti.v13i2.2124
Jurnal, S., Informatika, T., Komunikasi, I., April, N., Jl, A., Madya, R., Anyar, G., Anyar, K. G., & Timur, J. (2024). Analisis Sentimen Pada Pembatalan Tuan Rumah Indonesia Di Piala Dunia U-20 Menggunakan Fasttext Embeddings Dan Algoritma Recurrent Neural Network Aan Evian Nanda Universitas Pembangunan Nasional Veteran Jawa Timur Andreas Nugroho Sihananto Agung Mustika R. 2(2).
Liu, B. (2020). Sentiment Analysis: Mining Opinions, Sentiments, and Emotions, Second Edition. Sentiment Analysis: Mining Opinions, Sentiments, and Emotions, Second Edition, May, 1–432. https://doi.org/10.1017/9781108639286
Medhat, W., Hassan, A., & Korashy, H. (2014). Sentiment analysis algorithms and applications: A survey. Ain Shams Engineering Journal, 5(4), 1093–1113. https://doi.org/https://doi.org/10.1016/j.asej.2014.04.011
Mikolov, T., Yih, W. T., & Zweig, G. (2013). Linguistic Regularities in Continuous Space Word Representations. Proceedings of the 2nd Workshop on Computational Linguistics for Literature, CLfL 2013 at the 2013 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL- HLT 2013.
Pang, B., & Lee, L. (2008). Opinion Mining and Sentiment Analysis. Foundations and Trends® in Information Retrieval, 2(1–2), 1–135. https://doi.org/10.1561/1500000011
Prabowo, R., & Thelwall, M. (2009). Sentiment analysis: A combined approach. Journal of Informetrics, 3(2), 143–157. https://doi.org/https://doi.org/10.1016/j.joi.2009.01.003
Putri, D. R., Puspaningrum, E. Y., Maulana, H., Pembangunan, U., Veteran, N., & Timur, J. (2024). Indonesia Neural Network. 12(3), 2759–2769.
Restya, B., & Cahyono, N. (2025). Optimasi Metode Klasifikasi Menggunakan FastText dan Grid Search pada Aanalisi Sentimen Ulasan Aplikasi SeaBank Optimization of Classification Method Using FastText and Grid Search for Sentiment Analysis of SeaBank App Reviews. 1, 226–238. https://doi.org/10.26798/jiko.v9i1.1523
Speer, R., & Chin, J. (2016). An Ensemble Method to Produce High-Quality Word Embeddings (2016). http://arxiv.org/abs/1604.01692
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