摘要：我们提出了一套模块化数据科学流水线，用于从零散、非结构化的开源情报（OSINT）中估计公众对个人的情感倾向。该方法串联网页搜索、文本提取、相关性过滤、分词、共指消解和情感分析，将异构网络材料转换为可审计的个人级情感分布。我们将 AFINN 和 VADER 与 MINOS 进行比较；MINOS 是一种具备领域知识的情感分析算法，旨在检测与声誉风险、失当行为及积极公共贡献相关的语言。应用于具有已知声誉结果的公众人物时，MINOS 对正面、模糊和负面案例给出了最清晰的区分。结果表明，串联式 NLP 和 OSINT 方法能够为高风险决策支持提供透明、可复现且人机协同的情感评估。我们以英国荣誉制度展示了该方法；在这一制度中，个人须保持较高的公共行为标准，才能维持其荣誉。

Abstract: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.