An Efficient Fine-tuning of Generative Language Model for Aspect-Based Sentiment Analysis
Chaelyn Lee, Hanyong Lee, Kyumin Kim, Sojeong Kim, Jaesung Lee · 2024
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.