Data Science Approaches to Evaluating Honours Candidates

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.