Balanced Training Data Augmentation for Aspect-Based Sentiment Analysis

TL;DR

This paper introduces a novel LLM-based data augmentation method for aspect-based sentiment analysis that balances training data and improves model performance by optimizing synthetic data quality through reinforcement learning.

Contribution

It proposes a reinforcement learning approach to enhance data augmentation quality in LLM-based ABSA, addressing data imbalance and short text challenges.

Findings

Improved ABSA performance over strong baselines.

Balanced training data enhances model accuracy.

Reinforcement learning optimizes synthetic data quality.

Abstract

Aspect-based sentiment analysis (ABSA) is a crucial fine-grained task in social media scenarios to identify the sentiment polarity of specific aspect terms in a sentence. Although many existing studies leverage large language models (LLMs) to perform ABSA due to their strong context understanding capabilities, they still face challenges to learn the context information in the running text because of the short text, as well as the small and unbalanced labeled training data, where most data are labeled with positive sentiment. Data augmentation (DA) is a feasible strategy for providing richer contextual information, especially when using LLMs to create synthetic training data, but faces challenges in ensuring a high quality of the augmented data.In this paper, we propose an LLM-based ABSA approach with training data augmentation.Specifically, an LLM is prompted to generate augmented training data based on the original training data, so as to construct a new training data with larger size and balanced label distributions to better train an ABSA model. Meanwhile, in order to improve the quality of the augmented data, we propose a reinforcement learning approach to optimize the data augmentation. LLM.Experiment results and further analyses on English benchmark datasets for ABSA demonstrate the effectiveness of our approach, where superior performance is observed over strong baselines and most existing studies.