Deep Learning Neural Dalam Analisis Sentimen : Sebuah Studi Literatur
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Published:
Sep 30, 2025
Keywords:
Klasifikasi Sentime, neural network, prisma, nlp, literatur review
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Abstract
This study provides a thorough review of the literature on the application of deep learning in sentiment categorization. The major purpose is to collect statistical data on deep learning research for sentiment analysis and hybrid model construction. The research discovered that the most often utilized deep learning algorithms in 2022-2024 were BERT, LSTM, and GRU, each with varied degrees of accuracy. Specifically, GRU had the highest accuracy (98%), followed by LSTM (93.58%) and BERT (91.37%). In addition, 31% of the examined publications modified these methods to create new hybrid models. Among them, the RoBERTA and LSTM hybrid models achieved the highest accuracy (91.01%). This systematic review examines the changing landscape of sentiment analysis using deep learning, focusing on the efficacy of hybrid models in boosting classification accuracy.
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How to Cite
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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References
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[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.
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[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.
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[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.
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[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