# Fine-tuning Pretrained Multilingual BERT Model for Indonesian Aspect-based Sentiment Analysis

arXiv: https://arxiv.org/abs/2103.03732 · HTML: https://mlsanity.com/paper/2103.03732/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 10 references that are in the map.
- First posted on arXiv 5 Mar 2021.

## Builds on (most cited first, 4 of 4)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation](https://mlsanity.com/paper/1609.08144/) (2016)
- [Utilizing BERT for Aspect-Based Sentiment Analysis via Constructing Auxiliary Sentence](https://mlsanity.com/paper/1903.09588/) (2019)

## Built on by (most cited first, 4 of 4)

- [ReSynthDetect: A Fundus Anomaly Detection Network with Reconstruction and Synthetic Features](https://mlsanity.com/paper/2312.16470/) (2023)
- [Investigating Text Shortening Strategy in BERT: Truncation vs Summarization](https://mlsanity.com/paper/2403.12799/) (2024)
- [KPC-cF: Aspect-Based Sentiment Analysis via Implicit-Feature Alignment with Corpus Filtering](https://mlsanity.com/paper/2407.00342/) (2024)
- [LASQ: A Low-resource Aspect-based Sentiment Quadruple Extraction Dataset](https://mlsanity.com/paper/2604.10417/) (2026)

## Abstract (arXiv)

Although previous research on Aspect-based Sentiment Analysis (ABSA) for Indonesian reviews in hotel domain has been conducted using CNN and XGBoost, its model did not generalize well in test data and high number of OOV words contributed to misclassification cases. Nowadays, most state-of-the-art results for wide array of NLP tasks are achieved by utilizing pretrained language representation. In this paper, we intend to incorporate one of the foremost language representation model, BERT, to perform ABSA in Indonesian reviews dataset. By combining multilingual BERT (m-BERT) with task transformation method, we manage to achieve significant improvement by 8% on the F1-score compared to the result from our previous study.