摘要
目的针对特定推文情感分析任务中标注数据的困难和由于标注不准确带来的分类结果不尽如人意问题,提出一种机器标注数据的方法来研究深度学习模型对人工标注和机器标注推文数据情感分类的性能表现差异。方法研究中,对于统一的标签体系,分别对推文数据进行人工标注和运用ChatGPT模型接口标注,再采用BERT-TextCNN深度学习混合模型,对经过人工标注和ChatGPT标注的数据集进行情感分类。结果实验结果表明:人工标注数据集在整体性能上表现出更高的准确性和可信度,但是在某些推文数据上,ChatGPT大模型以其比人更丰富的知识储备,可以生成比人更客观科学的可解释性标注,在情感分类结果上呈现出一定的优势,人工标注和机器标注方法各具优劣;由此可以得出对于文本情感分类任务,机器标注是一种可行的标注方法。结论在实际应用场景中,可以根据任务需求灵活选择和结合两种标注方法,充分利用两者之间的优势,以达到更佳的分析性能和效果。
Objective This study addresses the challenges of annotating data for specific tweet sentiment analysis tasks and the issues arising from inaccurate annotations leading to unsatisfactory classification results.A method for machine annotation of data was proposed to investigate the performance differences in sentiment classification of tweets annotated by human annotators and the ChatGPT model.Methods In this study,for a unified labeling system,tweet data was annotated both manually and using the ChatGPT model interface,followed by sentiment classification using a BERT-TextCNN hybrid deep learning model on both datasets.Results Experimental results indicated that the manually annotated dataset exhibited higher overall accuracy and reliability.However,for certain tweet data,the ChatGPT model,with its richer knowledge base,can produce more objective and scientifically interpretable annotations,showing certain advantages in sentiment classification results.Both human and machine annotation methods have their strengths and weaknesses.Therefore,it can be concluded that machine annotation is a feasible labeling method for text sentiment classification tasks.Conclusion In practical applications,it is advisable to flexibly choose and combine both annotation methods based on task requirements and fully leverage the strengths of these two methods to achieve better analytical performance and outcomes.
作者
杨艺
黄镜月
贺品尧
荣婷
YANG Yi;HUANG Jingyue;HE Pinyao;RONG Ting(School of Artificial Intelligence,Chongqing Technology and Business University,Chongqing 400067,China;Chongqing Center for Research and Consultancy of Cyber Public Opinion and Ideological Development in Universities,Chongqing Technology and Business University,Chongqing 400067,China)
出处
《重庆工商大学学报(自然科学版)》
2025年第4期95-101,共7页
Journal of Chongqing Technology and Business University(Natural Science Edition)
基金
国家社会科学基金青年项目(22CZZ051)。
作者简介
杨艺(1971-),女,四川成都人,教授,CCF会员,从事大数据分析处理,信息系统开发研究.Email:yangchongyi@ctbu.edu.cn.
