Review of Aspect-Based Sentiment Analysis Based on Data Augmentation and Pre-Trained Models

Abstract

This paper systematically reviews the aspect-based sentiment analysis techniques that integrate data augmentation and pre-trained language models. Aspect-based sentiment analysis aims to identify the sentiment tendency of specific aspects in texts. Traditional methods face challenges such as data sparsity and insufficient model generalization. Data augmentation and pre-trained language models bring opportunities to solve these problems. Data augmentation can alleviate data sparsity, and pre-trained language models have powerful feature extraction and transfer learning capabilities. This paper elaborates on the task definition of aspect-based sentiment analysis, focusing on specific methods based on data augmentation and pre-trained language models, including data augmentation strategies and methods, as well as methods based on pre-trained language models such as BERT, RoBERTa, BART, and XLNet, and explores how to combine data augmentation and pre-trained models to improve the performance of aspect-level sentiment analysis. Finally, it is pointed out that there are still some challenges and opportunities in this field, such as the diversity of data augmentation techniques, optimization of pre-trained models, multimodal sentiment analysis, interpretability, and credibility, which need to be further explored.