Exploring ChatGPT-based Augmentation Strategies for Contrastive Aspect-based Sentiment Analysis
This work addresses data scarcity for researchers and practitioners in sentiment analysis, but it is incremental as it applies existing LLM methods to a specific domain task.
The paper tackled the challenge of scarce labeled data in aspect-based sentiment analysis by exploring ChatGPT-based data augmentation strategies, resulting in performance improvements with the context-aspect strategy achieving the best results and surpassing baseline models.
Aspect-based sentiment analysis (ABSA) involves identifying sentiment towards specific aspect terms in a sentence and allows us to uncover nuanced perspectives and attitudes on particular aspects of a product, service, or topic. However, the scarcity of labeled data poses a significant challenge to training high-quality models. To address this issue, we explore the potential of data augmentation using ChatGPT, a well-performing large language model (LLM), to enhance the sentiment classification performance towards aspect terms. Specifically, we explore three data augmentation strategies based on ChatGPT: context-focused, aspect-focused, and context-aspect data augmentation techniques. Context-focused data augmentation focuses on changing the word expression of context words in the sentence while keeping aspect terms unchanged. In contrast, aspect-focused data augmentation aims to change aspect terms but keep context words unchanged. Context-Aspect data augmentation integrates the above two data augmentations to generate augmented samples. Furthermore, we incorporate contrastive learning into the ABSA tasks to improve performance. Extensive experiments show that all three data augmentation techniques lead to performance improvements, with the context-aspect data augmentation strategy performing best and surpassing the performance of the baseline models.