M. Noer Fadli Hidayat, Didik Dwi Prasetya, Triyanna Widiyaningtyas
This study addresses the challenges of sentiment analysis on Indonesian-language YouTube comments, which are complex due to the use of dialects, slang words, emojis, and Latinized Arabic text. The proposed LABERT-LSTM model integrates BERT for deep feature extraction and Bi-LSTM to capture word sequence context effectively. The dataset comprises 24,593 YouTube comments from five renowned Islamic preachers discussing the topic of “tahlilan”. After data preprocessing, the model was evaluated using accuracy, precision, recall, and F1-score metrics. The results demonstrate that LABERT-LSTM achieved an accuracy of 0.95756, precision of 0.94014, recall of 0.91815, and an F1-score of 0.92868, outperforming standalone BERT and Bi-LSTM models by reducing misclassification and improving predictions for negative, positive, and neutral sentiment classes. Future research recommendations include expanding the dataset to other social media platforms, adopting advanced NLP techniques, conducting studies in other languages, and optimizing the model for enhanced performance and computational efficiency. © 2025, Intellectual Research and Development Education Foundation (YRPI). All rights reserved.
Department of Electrical Engineering and Informatics, Universitas Negeri Malang, Indonesia; Department of Informatics Engineering, Universitas Nurul Jadid Probolinggo, Indonesia
Research at a Glance
Register to unlock
Topics & SDG Alignment
Register to unlock
Collaboration
Register to unlock
Author Profile (Selected)
Register to unlock
References Overview
Register to unlock
Journal & Source
Register to unlock
Metadata & Integrity
Register to unlock