Systematic Literature Review dan Analisis Bibliometrik Analisis Sentimen di Media Sosial Berbasis Machine Learning pada Publikasi Scopus 2020–2025
DOI:
https://doi.org/10.52436/1.jpti.1376Kata Kunci:
deep learning, machine learning, sentiment analysis, social media, systematic literature review, VOSviewerAbstrak
Perkembangan era digital telah menghasilkan volume data yang sangat besar dari berbagai platform media sosial, yang merepresentasikan opini dan sentimen masyarakat terhadap beragam isu. Meskipun sentiment analysis berbasis machine learning dan Natural Language Processing (NLP) telah banyak diteliti, kajian yang secara komprehensif memetakan perkembangan penelitian, struktur keilmuan, serta arah riset masa depan melalui pendekatan Systematic Literature Review (SLR) dan analisis bibliometrik masih relatif terbatas. Penelitian ini bertujuan untuk mengeksplorasi lanskap penelitian sentiment analysis di media sosial berbasis machine learning, mengidentifikasi tren publikasi, aktor utama, serta klaster penelitian yang berkembang. Metode yang digunakan adalah Systematic Literature Review (SLR) dengan kerangka kerja PRISMA yang dipadukan dengan analisis bibliometrik menggunakan VOSviewer. Data dikumpulkan dari basis data Scopus hingga Agustus 2025 dan menghasilkan 666 artikel yang memenuhi kriteria inklusi. Hasil analisis menunjukkan adanya tren peningkatan publikasi yang konsisten pada periode 2020–2025, dengan India dan Amerika Serikat sebagai negara dengan kontribusi publikasi tertinggi, serta IEEE Access sebagai jurnal paling produktif. Analisis jaringan dan kata kunci menunjukkan dominasi topik sentiment analysis, machine learning, dan media sosial, disertai pergeseran menuju pendekatan deep learning dan NLP lanjutan. Secara keseluruhan, penelitian ini memberikan kontribusi akademik melalui pemetaan sistematis dan kuantitatif struktur penelitian sentiment analysis di media sosial, sekaligus memperkaya kajian SLR sebelumnya dengan perspektif bibliometrik. Temuan ini juga memiliki implikasi praktis bagi pengembangan riset dan penerapan sentiment analysis dalam bidang bisnis, kebijakan publik, dan pemasaran digital.
Unduhan
Referensi
S. Rokade, S. Aitwade, S. Pujari, and N. Kamble, “Unveiling the Power of Machine Learning: A Benchmark Analysis of Sentiment Analysis Methods and Their Real-World Performance,” in 15th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2024, 2024, vol. 2, pp. 3483–3488. [Online]. Available: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85209150763&partnerID=40&md5=2e5fb28b447743dd399ae05f91bbcd64
A. Pathak, K. V Arya, V. Tiwari, M. Bhende, and N. Barsagde, “Deciphering emotions: A comprehensive examination of machine learning algorithms for sentiment analysis,” in AIP Conference Proceedings, 2024, vol. 3214, no. 1. doi: 10.1063/5.0239531.
K. Renuka, D. Pachimatla, R. Kilari, I. B. Ranitha, I. Ranaveer, and K. Srinivasa Rao, “Social Media Analysis Using Machine Learning: Current Trends and Future Directions,” in Lecture Notes in Networks and Systems, 2025, vol. 1182, pp. 479–493. doi: 10.1007/978-981-97-8865-1_40.
J. R. Jim, M. A. R. Talukder, P. Malakar, M. M. Kabir, K. Nur, and M. F. Mridha, “Recent advancements and challenges of NLP-based sentiment analysis: A state-of-the-art review,” Nat. Lang. Process. J., vol. 6, no. February, p. 100059, 2024, doi: 10.1016/j.nlp.2024.100059.
N. Chotisarn and T. Phuthong, “A bibliometric analysis insights into the intellectual dynamics of artificial intelligence for the micro, small, and medium enterprises,” Cogent Bus. Manag., vol. 12, no. 1, p., 2025, doi: 10.1080/23311975.2025.2491684.
B. Ondara, S. Waithaka, J. Kandiri, and L. Muchemi, “Machine Learning Techniques, Features, Datasets, and Algorithm Performance Parameters for Sentiment Analysis: A Systematic Review,” Open J. Inf. Technol., vol. 5, no. 1, pp. 1–16, 2022, doi: 10.32591/coas.ojit.0501.01001o.
A. Zunic, P. Corcoran, and I. Spasic, “Sentiment analysis in health and well-being: Systematic review,” JMIR Med. Informatics, vol. 8, no. 1, pp. 1–22, 2020, doi: 10.2196/16023.
S. M. Sarsam, H. Al-Samarraie, A. I. Alzahrani, and B. Wright, “Sarcasm detection using machine learning algorithms in Twitter: A systematic review,” Int. J. Mark. Res., vol. 62, no. 5, pp. 578–598, 2020, doi: 10.1177/1470785320921779.
Q. A. Xu, V. Chang, and C. Jayne, “A systematic review of social media-based sentiment analysis: Emerging trends and challenges,” Decis. Anal. J., vol. 3, no. April, p. 100073, 2022, doi: 10.1016/j.dajour.2022.100073.
A. N. Ma’aly, D. Pramesti, A. D. Fathurahman, and H. Fakhrurroja, “Exploring Sentiment Analysis for the Indonesian Presidential Election Through Online Reviews Using Multi-Label Classification with a Deep Learning Algorithm,” Inf., vol. 15, no. 11, pp. 1–33, 2024, doi: 10.3390/info15110705.
S. Lai, X. Hu, H. Xu, Z. Ren, and Z. Liu, “Multimodal sentiment analysis: A survey,” Displays, vol. 80, 2023, doi: 10.1016/j.displa.2023.102563.
M. A. M. Hamidi, A. Y. Taqa, and Y. I. Ibrahim, “A Systematic Review of Multimodal Sentiment Analysis Based on Text-Image Fusion: Trends, Models, and Research Gaps,” Sinkron, vol. 9, no. 2, pp. 987–999, 2025, doi: 10.33395/sinkron.v9i2.14840.
N. M. Diaa, S. S. Ahmed, H. M. Salman, and W. A. Sajid, “Statistical Challenges in Social Media Data Analysis Sentiment Tracking and Beyond,” J. Ecohumanism, vol. 3, no. 5, pp. 365–384, 2024, doi: 10.62754/joe.v3i5.3912.
Q. Sun, L. Yang, C. Guo, and S. Lee, “Machine learning-based optimisation algorithms for sentiment analysis: an analysis of the state-of-the-art and upcoming challenges,” J. Exp. Theor. Artif. Intell., 2025, doi: 10.1080/0952813X.2025.2505421.
N. A. S. Abdullah, N. I. Shaari, and A. R. A. Rahman, “Review on sentiment analysis approaches for social media data,” J. Eng. Appl. Sci., vol. 12, no. 3, pp. 462–467, 2017, doi: 10.3923/jeasci.2017.462.467.
M. W. Habib and Z. N. Sultani, “A Review of Machine Learning Approach for Twitter Sentiment Analysis,” Al-Nahrain J. Sci., vol. 24, no. 4, pp. 52–58, 2021, doi: 10.22401/ANJS.24.4.08.
U. Jha, L. Tyagi, D. Kansal, S. Chakraborty, and A. Singhal, “A review of sentiment analysis techniques using soft computing approaches,” in Proceedings of the Confluence 2021: 11th International Conference on Cloud Computing, Data Science and Engineering, 2021, pp. 119–124. doi: 10.1109/Confluence51648.2021.9377031.
R. Dey, A. K. Ray, A. Srivastava, D. K. Sharma, and J. Piri, “Sentiment analysis using machine learning techniques with a digital dataset survey,” in ESIC 2025 - 5th International Conference on Emerging Systems and Intelligent Computing, Proceedings, 2025, pp. 410–414. doi: 10.1109/ESIC64052.2025.10962638.
B. B. Budisusetyo, W. A. Dhitya, A. Nugroho, and G. Pangestu, “Sentiment analysis methods recommendation: A review of aibased techniques on social media analysis,” in Procedia Computer Science, 2024, vol. 245, no. C, pp. 1120–1128. doi: 10.1016/j.procs.2024.10.341.
B. Ilyas and A. Sharifi, “A systematic review of social media-based sentiment analysis in disaster risk management,” Int. J. Disaster Risk Reduct., vol. 123, 2025, doi: 10.1016/j.ijdrr.2025.105487.
E. B. Ramezani, “Sentiment analysis applications using deep learning advancements in social networks: A systematic review,” Neurocomputing, vol. 634, 2025, doi: 10.1016/j.neucom.2025.129862.
A. Carrera-Rivera, W. Ochoa, F. Larrinaga, and G. Lasa, “How-to conduct a systematic literature review: A quick guide for computer science research,” MethodsX, vol. 9, p. 101895, 2022, doi: 10.1016/j.mex.2022.101895.
D. Holst, K. Moenck, J. Koch, O. Schmedemann, and T. Schüppstuhl, “Transparent Reporting of AI in Systematic Literature Reviews: Development of the PRISMA-trAIce Checklist.,” Jmir Ai, vol. 4, p. e80247, 2025, doi: 10.2196/80247.
O. Öztürk, R. Kocaman, and D. K. Kanbach, “How to design bibliometric research: an overview and a framework proposal,” Rev. Manag. Sci., vol. 18, no. 11, pp. 3333–3361, 2024, doi: 10.1007/s11846-024-00738-0.
B. Tedja, M. Al Musadieq, A. Kusumawati, and E. Yulianto, “Systematic literature review using PRISMA: exploring the influence of service quality and perceived value on satisfaction and intention to continue relationship,” Futur. Bus. J., vol. 10, no. 1, pp. 1–9, 2024, doi: 10.1186/s43093-024-00326-4.
A. R. Zuhri;, Erwindiawan;, and M. Abdusyakur Zulfan, “THE RESEARCH FIELD OF ISLAMIC BANK: A HYBRID SYSTEMATIC LITERATURE REVIEW AND BIBLIOMETRIC ANALYSIS,” LIKUID J. Ekon. Ind. Halal, vol. 5, no. 2, pp. 1–19, 2025.
A. Kristiana, M. Syaifulloh, and S. B. Riono, “Analisa Faktor Faktor Yang Mempengaruhi Kinerja Karyawan Di Rsu Islami Mutiara Bunda,” Value J. Manaj. dan Akunt., vol. 16, no. 1, pp. 104–114, 2021.
I. K. S. Al-Tameemi, M. R. Feizi-Derakhshi, S. Pashazadeh, and M. Asadpour, “A comprehensive review of visual–textual sentiment analysis from social media networks,” J. Comput. Soc. Sci., vol. 7, no. 3, pp. 2767–2838, 2024, doi: 10.1007/s42001-024-00326-y.
B. Feng, “Deep learning-based sentiment analysis for social media: A focus on multimodal and aspect-based approaches,” Appl. Comput. Eng., vol. 33, no. 1, pp. 1–8, 2024, doi: 10.54254/2755-2721/33/20230224.
M. Rodríguez-Ibánez, A. Casánez-Ventura, F. Castejón-Mateos, and P. M. Cuenca-Jiménez, “A review on sentiment analysis from social media platforms,” Expert Syst. Appl., vol. 223, no. August 2022, 2023, doi: 10.1016/j.eswa.2023.119862.
