The acute mania of King George III: A computational linguistic analysis.
We used a computational linguistic approach, exploiting machine learning techniques, to examine the letters written by King George III during mentally healthy and apparently mentally ill periods of his life. The aims of the study were: first, to establish the existence of alterations in the King's written language at the onset of his first manic episode; and secondly to identify salient sources of variation contributing to the changes. Effects on language were sought in two control conditions (politically stressful vs. politically tranquil periods and seasonal variation). We found clear differences in the letter corpus, across a range of different features, in association with the onset of mental derangement, which were driven by a combination of linguistic and information theory features that appeared to be specific to the contrast between acute mania and mental stability. The paucity of existing data relevant to changes in written language in the presence of acute mania suggests that lexical, syntactic and stylometric descriptions of written discourse produced by a cohort of patients with a diagnosis of acute mania will be necessary to support the diagnosis independently and to look for other periods of mental illness of the course of the King's life, and in other historically significant figures with similarly large archives of handwritten documents.
Neuroscience Research Centre, Molecular and Clinical Science Research Institute, St. George's, University of London (SGUL), London, United Kingdom.
Vassiliki
V
Rentoumi
Institute of Archaeology and Antiquity, University of Birmingham, Birmingham, United Kingdom.
Timothy
T
Peters
School of Humanities, University of Southampton, Southampton, United Kingdom.
Jonathan
J
Conlin
Neuroscience Research Centre, Molecular and Clinical Science Research Institute, St. George's, University of London (SGUL), London, United Kingdom.
Peter
P
Garrard
D001714:Bipolar Disorder / Q000175:diagnosis*; D005197:Famous Persons; D006801:Humans; D007802:Language; D008037:Linguistics / Q000379:methods; D000069550:Machine Learning; D008297:Male