计算机技术与发展2025，Vol.35Issue(5)：106-110,5.DOI:10.20165/j.cnki.ISSN1673-629X.2024.0391
基于ChatGLM3-6B的方面级情感分析研究
Research on Aspect-based Sentiment Analysis Based on ChatGLM3-6B
摘要
Abstract
To leverage the advantages of large language models,including the independence from labeled data,the excellent capabilities of contextual understanding and transfer learning,we apply LLM to the Unified Aspect-Based Sentiment Analysis(UABSA)task,which lacks labeled datasets.We propose a method called SLIT that combines data screening and LLM instruction tuning and have conducted experiments on Chinese and English datasets across various domains.Firstly,by comparing the similarity between samples in the training set,we filter out some highly similar samples to get a refined dataset.Secondly,we design a prompt template and construct an instruction dataset using the refined dataset.Finally,we apply P-Tuning v2 fine-tuning on the ChatGLM3-6B model and verify the results on the test set.The experiment shows that compared to the ChatGLM3-6B+fine-tuning method without data screening,SLIT improves the F1 score by 3.95 percentage points,2.01 percentage points,and 4.62 percentage points on the Rest14,Laptop 14,and robotic vacuum cleaner datasets,respectively.Compared to other baseline methods,SLIT achieves the best performance on the Rest 14 dataset.关键词
自然语言处理/方面级情感分析/大语言模型/数据筛选/指令微调Key words
natural language processing/aspect-based sentiment analysis/large language model/data screening/instruction tuning分类
信息技术与安全科学引用本文复制引用
孙晓晔,成彬,王程..基于ChatGLM3-6B的方面级情感分析研究[J].计算机技术与发展,2025,35(5):106-110,5.基金项目
河北省科学院高层次人才培养与资助项目(2024G18) （2024G18）
