A New Approach for Vietnamese Aspect-Based Sentiment Analysis

Abstract

Intelligent systems, especially smartphones, have become crucial parts of the world. These devices can solve various human tasks, from long-distance communication to healthcare assistants. For this tremendous success, customer feedback on a smartphone plays an integral role during the development process. This paper presents an improved approach for the Vietnamese Smartphone Feedback Dataset (UIT-ViSFD), collected and annotated carefully in 2021 (including 11,122 comments and their labels) by employing the pretrained PhoBERT model with a proper pre-processing method. In the experiments, we compare the approach with other transformer-based models such as XLM-R, DistilBERT, RoBERTa, and BERT. The experimental results show that the proposed method can bypass the state-of-the-art methods related to the UIT-ViSFD corpus. As a result, our model can achieve better macro-F1 scores for the Aspect and Sentiment Detection task, which are 86.03% and 78.76%, respectively. In addition, our approach could improve the results of Aspect-Based Sentiment Analysis datasets in the Vietnamese language.