大语言模型驱动的网络问政文本细粒度情感分析：一种多阶段优化方法

doi:10.3772/j.issn.1000-0135.2025.10.003

情报学报

2025, Vol. 44

Issue (10): 1242-1258 DOI: 10.3772/j.issn.1000-0135.2025.10.003

情报理论与方法

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大语言模型驱动的网络问政文本细粒度情感分析：一种多阶段优化方法

滕婕^1,2, 贺荒兰^1,2, 胡广伟^1,2

1.南京大学信息管理学院，南京 210023
2.南京大学政务数据资源研究所，南京 210023

Fine-Grained Sentiment Analysis of Online Government- Public Interaction Texts Using Large Language Models: Multistage Optimization Approach

Teng Jie^1,2, He Huanglan^1,2, Hu Guangwei^1,2

1.School of Information Management, Nanjing University, Nanjing 210023
2.Government Data Resources Institution of Nanjing University, Nanjing 210023

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摘要网络问政文本蕴含丰富的民意情感信息，其细粒度情感分析对提升政府治理能力具有重要价值。然而，传统方法难以准确捕捉文本中的复杂情感特征。本研究提出了一种基于大语言模型的网络问政文本细粒度情感分析多阶段优化框架。首先，基于情绪唤醒度-效价理论，构建适合网络问政场景的8个情绪区间和56个情绪标签的情感分类体系，并采用GPT-4（generative pre-trained transformer 4）配合BROKE（background，role，objectives，key result，evolve）框架设计的提示策略进行初始情感标注，有效地解决了复杂语境理解挑战；其次，创新性地提出“大语言模型标注-专家评估-研判模型训练-多阶段优化”的数据质量控制机制，通过Claude 3.5 Sonnet模型校正和研判模型筛选，解决大语言模型在领域应用中的“幻觉”问题；最后，基于Claude 3.5 Sonnet的数据生成能力实现情感类别均衡，将不平衡基尼系数从0.866降至0.181，显著提升模型泛化能力。实验结果表明，该框架在情绪细粒度分析任务中准确率达89.70%，F1值较现有方法平均提高21.65%，为政民互动效能提升提供了新的技术范式。

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关键词 ：网络问政, 细粒度情感, 大语言模型, 数据质量评估, 数据均衡

收稿日期: 2024-11-28

基金资助:国家社会科学基金重大项目“大数据驱动的城乡社区服务体系精准化构建研究”（20&ZD154）。

作者简介: 滕婕，1993年生，博士研究生，主要研究方向为自然语言处理、大模型应用实践；贺荒兰，2001年生，硕士研究生，主要研究方向为网络问政；胡广伟，通信作者，1975年生，博士，教授，博士生导师，主要研究方向为政务大数据、数据智能与数据治理、价值共创等，E-mail：hugw@nju.edu.cn；

引用本文:

滕婕, 贺荒兰, 胡广伟. 大语言模型驱动的网络问政文本细粒度情感分析：一种多阶段优化方法[J]. 情报学报, 2025, 44(10): 1242-1258.
Teng Jie, He Huanglan, Hu Guangwei. Fine-Grained Sentiment Analysis of Online Government- Public Interaction Texts Using Large Language Models: Multistage Optimization Approach. 情报学报, 2025, 44(10): 1242-1258.

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https://qbxb.istic.ac.cn/CN/10.3772/j.issn.1000-0135.2025.10.003 或 https://qbxb.istic.ac.cn/CN/Y2025/V44/I10/1242

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