Deep Learning Neural Dalam Analisis Sentimen : Sebuah Studi Literatur
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Sep 30, 2025
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Klasifikasi Sentime, neural network, prisma, nlp, literatur review
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Studi ini memberikan tinjauan mendalam terhadap literatur mengenai penerapan deep learning dalam kategorisasi sentimen. Tujuan utamanya adalah untuk mengumpulkan data statistik terkait penelitian deep learning untuk analisis sentimen dan pengembangan model hibrida. Penelitian ini menemukan bahwa algoritma deep learning yang paling sering digunakan pada tahun 2022–2024 adalah BERT, LSTM, dan GRU, masing-masing dengan tingkat akurasi yang bervariasi. Secara khusus, GRU menunjukkan akurasi tertinggi (98%), diikuti oleh LSTM (93,58%) dan BERT (91,37%). Selain itu, sebanyak 31% dari publikasi yang ditinjau memodifikasi metode ini untuk membentuk model hibrida baru. Di antara model-model tersebut, kombinasi RoBERTA dan LSTM mencatatkan akurasi tertinggi sebesar 91,01%. Tinjauan sistematis ini mengeksplorasi perkembangan lanskap analisis sentimen dengan pendekatan deep learning, dengan fokus pada efektivitas model hibrida dalam meningkatkan akurasi klasifikasi
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Yanuargi, B., Utami, E., Kusrini, & Parikesit, A. A. (2025). Deep Learning Neural Dalam Analisis Sentimen : Sebuah Studi Literatur. JITSI : Jurnal Ilmiah Teknologi Sistem Informasi, 6(3), 321 - 328. https://doi.org/10.62527/jitsi.6.3.499
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Referensi
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[21] V. Diviya Prabha and R. Rathipriya, “Competitive Capsule Network Based Sentiment Analysis on Twitter COVID’19 Vaccines,” Journal of Web Engineering, vol. 21, no. 5, pp. 1583–1602, 2022, doi: 10.13052/jwe1540-9589.2159.
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[2] N. C. Dang, M. N. Moreno-García, and F. De la Prieta, “Sentiment analysis based on deep learning: A comparative study,” Electronics (Switzerland), vol. 9, no. 3, Mar. 2020, doi: 10.3390/electronics9030483.
[3] C.-G. Kim, Y.-J. Hwang, and C. Kamyod, “A Study of Profanity Effect in Sentiment Analysis on Natural Language Processing Using ANN,” Journal of Web Engineering, vol. 21, no. 3, pp. 751–766, 2022, doi: 10.13052/jwe1540-9589.2139.
[4] M. Shukla and A. Kumar, “An Experimental Analysis of Deep Neural Network Based Classifiers for Sentiment Analysis Task,” IEEE Access, vol. 11, pp. 36929–36944, 2023, doi: 10.1109/ACCESS.2023.3266640.
[5] M. Bilal, A. Khan, S. Jan, and S. Musa, “Context-Aware Deep Learning Model for Detection of Roman Urdu Hate Speech on Social Media Platform,” IEEE Access, vol. 10, pp. 121133–121151, 2022, doi: 10.1109/ACCESS.2022.3216375.
[6] K. L. Tan, C. P. Lee, K. S. M. Anbananthen, and K. M. Lim, “RoBERTa-LSTM: A Hybrid Model for Sentiment Analysis With Transformer and Recurrent Neural Network,” IEEE Access, vol. 10, pp. 21517–21525, 2022, doi: 10.1109/ACCESS.2022.3152828.
[7] Y. Li and N. Li, “Sentiment Analysis of Weibo Comments Based on Graph Neural Network,” IEEE Access, vol. 10, pp. 23497–23510, 2022, doi: 10.1109/ACCESS.2022.3154107.
[8] K. Jahanbin and M. A. Z. Chahooki, “Aspect-Based Sentiment Analysis of Twitter Influencers to Predict the Trend of Cryptocurrencies Based on Hybrid Deep Transfer Learning Models,” IEEE Access, vol. 11, pp. 121656–121670, 2023, doi: 10.1109/ACCESS.2023.3327060.
[9] A. Altaf et al., “Deep Learning Based Cross Domain Sentiment Classification for Urdu Language,” IEEE Access, vol. 10, pp. 102135–102147, 2022, doi: 10.1109/ACCESS.2022.3208164.
[10] N. Aslam, F. Rustam, E. Lee, P. B. Washington, and I. Ashraf, “Sentiment Analysis and Emotion Detection on Cryptocurrency Related Tweets Using Ensemble LSTM-GRU Model,” IEEE Access, vol. 10, pp. 39313–39324, 2022, doi: 10.1109/ACCESS.2022.3165621.
[11] C. G. Kim, Y. J. Hwang, and C. Kamyod, “A Study of Profanity Effect in Sentiment Analysis on Natural Language Processing Using ANN,” Journal of Web Engineering, vol. 21, no. 3, pp. 751–766, 2022, doi: 10.13052/jwe1540-9589.2139.
[12] N. Aslam, F. Rustam, E. Lee, P. B. Washington, and I. Ashraf, “Sentiment Analysis and Emotion Detection on Cryptocurrency Related Tweets Using Ensemble LSTM-GRU Model,” IEEE Access, vol. 10, pp. 39313–39324, 2022, doi: 10.1109/ACCESS.2022.3165621.
[13] J. Mao, Z. Qian, and T. Lucas, “Sentiment Analysis of Animated Online Education Texts Using Long Short-Term Memory Networks in the Context of the Internet of Things,” IEEE Access, vol. 11, pp. 109121–109130, 2023, doi: 10.1109/ACCESS.2023.3321303.
[14] H. T. Phan, N. T. Nguyen, and D. Hwang, “Aspect-Level Sentiment Analysis Using CNN Over BERT-GCN,” IEEE Access, vol. 10, pp. 110402–110409, 2022, doi: 10.1109/ACCESS.2022.3214233.
[15] M. Yekrangi and N. S. Nikolov, “Domain-Specific Sentiment Analysis: An Optimized Deep Learning Approach for the Financial Markets,” IEEE Access, vol. 11, pp. 70248–70262, 2023, doi: 10.1109/ACCESS.2023.3293733.
[16] J. K. Adarsh, V. T. Sreedevi, and D. Thangavelusamy, “Product Review System With BERT for Sentiment Analysis and Implementation of Administrative Privileges on Node-RED,” IEEE Access, vol. 11, pp. 65968–65976, 2023, doi: 10.1109/ACCESS.2023.3275738.
[17] R. Chandra and V. Kulkarni, “Semantic and Sentiment Analysis of Selected Bhagavad Gita Translations Using BERT-Based Language Framework,” IEEE Access, vol. 10, pp. 21291–21315, 2022, doi: 10.1109/ACCESS.2022.3152266.
[18] S. Mifrah and E. H. Benlahmar, “Sentence-Level Sentiment Classification A Comparative Study between Deep Learning Models,” Journal of ICT Standardization, vol. 10, no. 2, pp. 339–352, 2022, doi: 10.13052/jicts2245-800X.10213.
[19] J. Chen, H. Fan, and W. Wang, “Syntactic and Semantic Aware Graph Convolutional Network for Aspect-Based Sentiment Analysis,” IEEE Access, vol. 12, pp. 22500–22509, 2024, doi: 10.1109/ACCESS.2024.3364353.
[20] P. Durga and D. Godavarthi, “Deep-Sentiment: An Effective Deep Sentiment Analysis Using a Decision-Based Recurrent Neural Network (D-RNN),” IEEE Access, vol. 11, pp. 108433–108447, 2023, doi: 10.1109/ACCESS.2023.3320738.
[21] V. Diviya Prabha and R. Rathipriya, “Competitive Capsule Network Based Sentiment Analysis on Twitter COVID’19 Vaccines,” Journal of Web Engineering, vol. 21, no. 5, pp. 1583–1602, 2022, doi: 10.13052/jwe1540-9589.2159.
[22] S. Gundapu and R. Mamidi, “Transformer based Automatic COVID-19 Fake News Detection System,” Jan. 2021, [Online]. Available: http://arxiv.org/abs/2101.00180