KPC-cF: Aspect-Based Sentiment Analysis via Implicit-Feature Alignment with Corpus Filtering

TL;DR

This paper introduces a novel aspect-based sentiment analysis framework for Korean, leveraging implicit feature alignment and corpus filtering to improve performance in low-resource language settings.

Contribution

It presents a new method combining translation, pseudo-labeling, and filtering techniques to enhance ABSA in Korean, a low-resource language, with minimal labeled data.

Findings

Achieved approximately 3% improvement in F1 scores and accuracy over baseline models.

Developed a pipeline that effectively bridges dataset gaps in low-resource language ABSA.

Released dataset and code for Korean ABSA to facilitate future research.

Abstract

Investigations into Aspect-Based Sentiment Analysis (ABSA) for Korean industrial reviews are notably lacking in the existing literature. Our research proposes an intuitive and effective framework for ABSA in low-resource languages such as Korean. It optimizes prediction labels by integrating translated benchmark and unlabeled Korean data. Using a model fine-tuned on translated data, we pseudo-labeled the actual Korean NLI set. Subsequently, we applied LaBSE and \MSP{}-based filtering to this pseudo-NLI set as implicit feature, enhancing Aspect Category Detection and Polarity determination through additional training. Incorporating dual filtering, this model bridged dataset gaps and facilitates feature alignment with minimal resources. By implementing alignment pipelines, our approach aims to leverage high-resource datasets to develop reliable predictive and refined models within corporate or individual communities in low-resource language countries. Compared to English ABSA, our framework showed an approximately 3\% difference in F1 scores and accuracy. We will release our dataset and code for Korean ABSA, at this link.