# Fine-Tuning BERT for Sentiment Analysis of Vietnamese Reviews

arXiv: https://arxiv.org/abs/2011.10426 · HTML: https://mlsanity.com/paper/2011.10426/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Nov 2020.

## Built on by (most cited first, 6 of 6)

- [Test2Vec: An Execution Trace Embedding for Test Case Prioritization](https://mlsanity.com/paper/2206.15428/) (2022)
- [BERT Fine-Tuning for Sentiment Analysis on Indonesian Mobile Apps Reviews](https://mlsanity.com/paper/2107.06802/) (2021)
- [Vietnamese Capitalization and Punctuation Recovery Models](https://mlsanity.com/paper/2207.01312/) (2022)
- [ReINTEL Challenge 2020: Exploiting Transfer Learning Models for Reliable Intelligence Identification on Vietnamese Social Network Sites](https://mlsanity.com/paper/2102.10794/) (2021)
- [Is word segmentation necessary for Vietnamese sentiment classification?](https://mlsanity.com/paper/2301.00418/) (2023)
- [Learning for Amalgamation: A Multi-Source Transfer Learning Framework For Sentiment Classification](https://mlsanity.com/paper/2303.09115/) (2023)

## Abstract (arXiv)

Sentiment analysis is an important task in the field ofNature Language Processing (NLP), in which users' feedbackdata on a specific issue are evaluated and analyzed. Manydeep learning models have been proposed to tackle this task, including the recently-introduced Bidirectional Encoder Rep-resentations from Transformers (BERT) model. In this paper,we experiment with two BERT fine-tuning methods for thesentiment analysis task on datasets of Vietnamese reviews: 1) a method that uses only the [CLS] token as the input for anattached feed-forward neural network, and 2) another methodin which all BERT output vectors are used as the input forclassification. Experimental results on two datasets show thatmodels using BERT slightly outperform other models usingGloVe and FastText. Also, regarding the datasets employed inthis study, our proposed BERT fine-tuning method produces amodel with better performance than the original BERT fine-tuning method.