  Aspect-based Sentiment Analysis (ABSA) is an important sentiment analysis
task, which aims to determine the sentiment polarity towards an aspect in a
sentence. Due to the expensive and limited labeled data, data augmentation (DA)
has become the standard for improving the performance of ABSA. However, current
DA methods usually have some shortcomings: 1) poor fluency and coherence, 2)
lack of diversity of generated data, and 3) reliance on some existing labeled
data, hindering its applications in real-world scenarios. In response to these
problems, we propose a systematic Iterative Data augmentation framework, namely
IterD, to boost the performance of ABSA. The core of IterD is to leverage the
powerful ability of large language models (LLMs) to iteratively generate more
fluent and diverse synthetic labeled data, starting from an unsupervised
sentence corpus. Extensive experiments on 4 widely-used ABSA benchmarks show
that IterD brings consistent and significant performance gains among 5 baseline
ABSA models. More encouragingly, the synthetic data generated by IterD can
achieve comparable or even better performance against the manually annotated
data.
