发表机构
Ministry of Justice; Joint Counter-Terrorism Prisons and Probation Hub; Arndit Ltd.; Indian Institute of Technology Delhi; Kainos（司法部; 联合反恐监狱与缓刑中心; 阿恩迪特有限公司; 印度德里理工学院; 凯诺斯公司）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究提出模块化数据科学流程，结合 NLP 与 OSINT 开发 MINOS 情感算法，可对公众人物情感进行可审计评估，在英国荣誉制度中验证了其对高风险决策的支持作用。
AI 中文摘要
我们提出了一种模块化数据科学流程，用于从碎片化、非结构化的开源情报（OSINT）中估算公众对个人的 sentiment。该方法将网络搜索、文本提取、相关性过滤、分词、共指消解和情感分析串联起来，将异构网络材料转化为可审计的个人层面情感分布。我们将 AFINN 和 VADER 与 MINOS 进行比较，MINOS 是一种领域知情的情感算法，旨在检测与声誉风险、不当行为和积极公众贡献相关的语言。将其应用于具有已知声誉结果的公众人物时，MINOS 能最清晰地区分积极、模糊和消极案例。结果表明，串联的 NLP 和 OSINT 方法可支持高风险决策所需的透明、可复现、人机协作的情感评估。我们在英国荣誉制度上展示了该方法，在该制度中，个人需展现出高标准的公众行为以维持其荣誉。
英文摘要
We present a modular data-science pipeline for estimating public sentiment towards individuals from fragmented, unstructured open-source intelligence (OSINT). The method chains web search, text extraction, relevance filtering, tokenisation, co-reference resolution, and sentiment analysis to convert heterogeneous web material into auditable person-level sentiment distributions. We compare AFINN and VADER with MINOS, a domain-informed sentiment algorithm designed to detect language associated with reputational risk, misconduct, and positive public contribution. Applied to public figures with known reputational outcomes, MINOS gives the clearest separation between positive, ambiguous, and negative cases. The results show that chained NLP and OSINT methods can support transparent, reproducible, human-in-the-loop sentiment assessment for high-stakes decision support. We demonstrate the approach on the UK Honours system, where individuals are required to display high standards of public conduct to maintain an Honour.
Comments13 pages, 6 figures, corrects typographical errors from published version and includes full-colour figures
Journal refArtificial Intelligence XLII. SGAI-AI 2025. Lecture Notes in Computer Science, vol. 16302, pp. 330-343 (2026)
DOI：10.1007/978-3-032-11442-6_23
