Data Science Approaches to Evaluating Honours Candidates
Read the original on arXiv Computation and Language →The paper introduces a modular data‑science pipeline that estimates public sentiment toward individuals using fragmented, unstructured open‑source intelligence. The pipeline combines web search, text extraction, relevance filtering, tokenisation, co‑reference resolution, and sentiment analysis to produce auditable person‑level sentiment distributions. By comparing AFINN, VADER, and the domain‑specific MINOS algorithm, the authors show that MINOS best distinguishes positive, ambiguous, and negative reputational cases, and they apply the method to the UK Honours system to support transparent, reproducible, human‑in‑the‑loop sentiment assessment for high‑stakes decisions.
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