Abstract

This paper explores the application of data science to the UK Honours system. We have identified the key challenges which need to be overcome before data science can be applied to increase public confidence in the Honours System. In this paper we describe a system for scraping the internet for publicly available information about applicants to the Honours system and then attempting to form an opinion about them using machine intelligence. In order to form an opinion about applicants for the UK King's Honours, we have evaluated two existing sentiment algorithms (afinn, vader and then created our own novel algorithm minos). Various other Natural techniques )(E.g. Coreference resolution) have then been explored and their impact or improvements to the performance and efficacy of our system has been compared. The promising results in this work indicate that this system can be used to augment human evaluation to better judge the suitability of candidates for receiving honours and awards. In addition it can avoid major faux-pas by granting awards without thoroughly investigating the backgrounds of candidates. The system can be extended in future for other awards and recognition programs.