Data Science Approaches to Evaluating Honours Candidates
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Computer Science > Computation and Language
Title:Data Science Approaches to Evaluating Honours Candidates
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.
| Comments: | 13 pages, 6 figures, corrects typographical errors from published version and includes full-colour figures |
| Subjects: | Computation and Language (cs.CL) |
| Cite as: | arXiv:2608.26135 [cs.CL] |
| (or arXiv:2608.26135v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2608.26135
arXiv-issued DOI via DataCite
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| Journal reference: | Artificial Intelligence XLII. SGAI-AI 2025. Lecture Notes in Computer Science, vol. 16302, pp. 330-343 (2026) |
| Related DOI: | https://doi.org/10.1007/978-3-032-11442-6_23
DOI(s) linking to related resources
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Submission history
From: Francesca Von Braun-Bates [view email][v1] Thu, 25 Jun 2026 14:48:44 UTC (115 KB)
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