Bong-Su Kim  Seung-Ho Choi  Si-hyun Park  Jun-Ho Wang  Ji-Yoon Kim  Hyun-Kyu Jeon  Jung-Hoon Jang

10.5626/JOK.2024.51.12.1043

Aspect-Based Sentiment Analysis prompt tuning Few-Shot Learning dependency parsing

Aspect-based sentiment analysis examines how emotions in text relate to specific aspects, such as product characteristics or service features. This paper presents a comprehensive methodology for applying prompt tuning techniques to multi-task token labeling challenges using aspect-based sentiment analysis data. The methodology includes a pipeline for identifying emotion expression domains, which generalizes the token labeling problem into a sequence labeling problem. It also suggests selecting templates to classify separated sequences based on aspects and emotions, and expanding label words to align with the dataset’s characteristics, thus optimizing the model's performance. Finally, the paper provides several experimental results and analyses for the aspect-based sentiment analysis task in a few-shot setting. The constructed data and baseline model are available on AIHUB. (www.aihub.or.kr).