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Bahari, Aris Rifki Setiya
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Systematic Review of Hyperparameter Adjustment and Evaluation Metrics in Bert-Based Sentiment Analysis Bahari, Aris Rifki Setiya; Utomo, Fandy Setyo; Berlilana, Berlilana
Journal La Multiapp Vol. 7 No. 2 (2026): Journal La Multiapp
Publisher : Newinera Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37899/journallamultiapp.v7i2.3046

Abstract

The development of sentiment analysis towards Aspect-Based Sentiment Analysis (ABSA) has made significant progress thanks to deep learning technology, especially through the Bidirectional Encoder Representations from Transformers (BERT) architecture. Despite its increasing popularity, a comprehensive synthesis of global research patterns and optimal model configurations is still urgently needed. This study presents a Systematic Literature Review (SLR) combined with bibliometric analysis to examine BERT-based ABSA research indexed in Scopus. Using the PRISMA and VOSviewer frameworks for visualization, a total of 62 eligible articles up to mid-2025 were analyzed. The results of the study show a strong upward trend of publications with a peak in 2024, where China, India, and Indonesia emerged as the major contributors in this domain. Further, the review identified a critical technical standard for effective model training: the Adam optimizer was the most dominant choice, typically paired with a learning rate between 1e-5 to 2e-5 and a batch size of 16. Regarding performance evaluation, Accuracy and F1-Score are set as de facto standard metrics. These findings provide strategic guidance for researchers to optimize BERT implementation and identify future directions in more in-depth sentiment analysis tasks.
Co-Authors Berlilana Berlilana Fandy Setyo Utomo