Expanding Vietnamese SentiWordNet to Improve Performance of Vietnamese Sentiment Analysis Models

TL;DR

This paper enhances Vietnamese sentiment analysis by expanding SentiWordNet and combining it with PhoBERT-V2, leading to improved performance on benchmark datasets.

Contribution

It introduces a novel method integrating expanded SentiWordNet with PhoBERT-V2 for Vietnamese sentiment analysis, achieving superior results.

Findings

Improved accuracy on VLSP 2016 dataset

Enhanced performance on AIVIVN 2019 dataset

Outperforms existing models in Vietnamese sentiment analysis

Abstract

Sentiment analysis is one of the most crucial tasks in Natural Language Processing (NLP), involving the training of machine learning models to classify text based on the polarity of opinions. Pre-trained Language Models (PLMs) can be applied to downstream tasks through fine-tuning, eliminating the need to train the model from scratch. Specifically, PLMs have been employed for Sentiment Analysis, a process that involves detecting, analyzing, and extracting the polarity of text sentiments. Numerous models have been proposed to address this task, with pre-trained PhoBERT-V2 models standing out as the state-of-the-art language models for Vietnamese. The PhoBERT-V2 pre-training approach is based on RoBERTa, optimizing the BERT pre-training method for more robust performance. In this paper, we introduce a novel approach that combines PhoBERT-V2 and SentiWordnet for Sentiment Analysis of Vietnamese reviews. Our proposed model utilizes PhoBERT-V2 for Vietnamese, offering a robust optimization for the prominent BERT model in the context of Vietnamese language, and leverages SentiWordNet, a lexical resource explicitly designed to support sentiment classification applications. Experimental results on the VLSP 2016 and AIVIVN 2019 datasets demonstrate that our sentiment analysis system has achieved excellent performance in comparison to other models.