Fine-tuned PhoBERT for sentiment analysis of Vietnamese phone reviews

Abstract

This paper presents an exploration of sentiment analysis applied to Vietnamese phone reviews, leveraging the PhoBERT model. While significant advancements have been made in sentiment analysis for English and other widely spoken languages, Vietnamese remains relatively under investigated. Our study addresses this gap by constructing a comprehensive dataset that integrates data from the UIT-ViSFD dataset and data collected through web scraping. We experimented with various models including naive Bayes, Support Vector Machine, and PhoBERT, utilizing multiple data preprocessing techniques. PhoBERT, a state-of-the-art pre-trained language model specifically designed for Vietnamese, demonstrated superior performance. The final PhoBERT model with optimized preprocessing achieved an accuracy of 92.74%, highlighting its efficacy in accurately identifying sentiments.