Analysis of Twitter User Sentiment towards the 2024 Election Based on the XLM-T Model
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Abstract
In the current digital era, social media, especially Twitter, has become an important platform for people to share opinions, especially regarding political issues such as the 2024 Presidential Election (Pemilu) in Indonesia. This research aims to analyze public sentiment regarding the 2024 Election based on collected tweet data. By using an XLM-T based machine learning model, this research succeeded in classifying tweets into three sentiment categories: positive, negative and neutral with a model accuracy rate of 68%. The results show that tweets with positive and negative sentiments receive more interaction from the public compared to tweets with neutral sentiments, indicating the public's tendency to more actively interact with opinions that have a certain position or stance on an issue. In conclusion, sentiment analysis can provide deep insight into the public's views on the 2024 Election, which political stakeholders can utilize in designing their campaign strategies.
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Ahmadi, A. (2015): Analisis Konstruksi Hukum Konstitusionalitas Pemilu Serentak Pada Tahun 2019. Al-'Adl, 8(1), 1-19
Akram, W., & Kumar, R. (2017): A Study on Positive and Negative Effects of Social Media on Society. International Journal of Computer Sciences and Engineering, 5(10), 351–354. https://doi.org/10.26438/ijcse/v5i10.351354
Alasadi, S. A., & Bhaya, W. S. (2017). Review of data preprocessing techniques in data mining. Journal of Engineering and Applied Sciences, 12(16), 4102-4107.
Barbieri, F., Anke, L. E., & Camacho-Collados, J. (2022): XLM-T: Multilingual Language Models in Twitter for Sentiment Analysis and Beyond (pp. 258–266). European Language Resources Association. https://aclanthology.org/2022.lrec-1.27/
Indonesia, Data. “Pengguna Media Sosial Di Indonesia Sebanyak 167 Juta Pada 2023.” Dataindonesia.id, 3 Feb. 2023, dataindonesia.id/internet/detail/pengguna-media-sosial-di-indonesia-sebanyak-167-juta-pada-2023. Diakses pada 19 September 2023, dari https://dataindonesia.id/internet/detail/pengguna-media-sosial-di-indonesia-sebanyak-167-juta-pada-2023
Logunova, I. (2023, July 11): F1 Score in Machine Learning. Serokell Software Development Company. Diakses pada 26 September 2023, dari https://serokell.io/blog/a-guide-to-f1-score
Muhammad Rizano Lukman, Muhammad Farrih Mahabbataka Arsyada, Mochamad Rafli Ghufron, Yudhistira Azhar Haryono Putra, & Nur Aini Rakhmawati. (2023). Dataset Analisis Sentimen Twitter Pada Pemilu 2024 (1.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.8392888
Munir, M., Fauzi, M., & Perdana, R. (2018): Implementasi Metode Backpropagation Neural Network berbasis Lexicon Based Features dan Bag of Words Untuk Identifikasi Ujaran Kebencian Pada Twitter. Jurnal Pengembangan Teknologi Informasi Dan Ilmu Komputer, 2(10), 3182–3191.
Nurhuda, F., Widya Sihwi, S., & Doewes, A. (2016). Analisis Sentimen Masyarakat terhadap Calon Presiden Indonesia 2014 berdasarkan Opini dari Twitter Menggunakan Metode Naive Bayes Classifier. Jurnal Teknologi & Informasi ITSmart, 2(2), 35. https://doi.org/10.20961/its.v2i2.630
Rivaldi, A. A., Azra, B., Ziaulhaq, Y. I., & Rakhmawati, N. A. (2022): Analisis Karakteristik Akun Twitter Berdasarkan Sentimen Pendapat Terkait Undang-Undang PSE. SATIN – Sains Dan Teknologi Informasi, 8(2). https://doi.org/10.33372/stn.v8i2.876
Rozi, I. F., Pramitarini, Y., & Puspitasari, N. (2020): Analisis Mengenai Calon Presiden Indonesia 2019 Di Twitter Menggunakan Metode Backpropagation. Jurnal Informatika Polinema, 6(2).
Vimal B.(2020). Application of Logistic Regression in Natural Language Processing [Online]. www.ijert.org .