An Efficient Fine-tuning of Generative Language Model for Aspect-Based Sentiment Analysis
- Authors
- Lee, Chaelyn; Lee, Hanyong; Kim, Kyumin; Kim, Sojeong; Lee, Jaesung
- Issue Date
- Jan-2024
- Publisher
- Institute of Electrical and Electronics Engineers Inc.
- Keywords
- Aspect-Based Sentiment Analysis; Fine-tuning; Generative Language Model; GPT; LoRA
- Citation
- Digest of Technical Papers - IEEE International Conference on Consumer Electronics, v.2024 IEEE
- Journal Title
- Digest of Technical Papers - IEEE International Conference on Consumer Electronics
- Volume
- 2024 IEEE
- URI
- https://scholarworks.bwise.kr/cau/handle/2019.sw.cau/73052
- DOI
- 10.1109/ICCE59016.2024.10444216
- ISSN
- 0747-668X
- Abstract
- Sentiment analysis is considered as an important study where be able to automatically extract the polarity of consumers or users' opinions and use it as important data for decision-making in companies or organizations. It has further developed into Aspect-Based Sentiment Analysis research that predicts the polarity for a specific aspect within a sentence. Recently, research has been conducted to convert emotion analysis based on classification work to a model that obtains more diverse and accurate emotion expressions using generative language models. We propose a method of fine-tuning by introducing Low-Rank Adaptation (LoRA) into a generative language model to improve the performance of these generative-based ABSA models and enable efficient learning. In this paper, GloABSA (GPT2+LoRA Aspect-Based Sentiment Analysis) aims at improving the learning efficiency of the previously proposed GPTABSA model. In this study, LoRA is introduced and fine-tuned to the GPT2 model to predict aspects and polarities using enhanced contextual information, and to reduce the number of parameters to enable efficient learning. Experiments using a benchmark dataset of ABSA, let us show that our proposed method outperforms previous studies and significantly reduces the number of parameters. © 2024 IEEE.
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Collections - College of Software > Department of Artificial Intelligence > 1. Journal Articles
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