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Multidimensional Data Analysis of Deep–Language in J.R.R. Tolkien and C.S. Lewis Reveals Tight Mathematical Connections
Version 1
: Received: 17 June 2024 / Approved: 17 June 2024 / Online: 18 June 2024 (12:18:06 CEST)
A peer-reviewed article of this Preprint also exists.
Matricciani, E. Multi–Dimensional Data Analysis of Deep Language in J.R.R. Tolkien and C.S. Lewis Reveals Tight Mathematical Connections. AppliedMath 2024, 4, 927-949. Matricciani, E. Multi–Dimensional Data Analysis of Deep Language in J.R.R. Tolkien and C.S. Lewis Reveals Tight Mathematical Connections. AppliedMath 2024, 4, 927-949.
Abstract
Scholars of the English Literature unanimously say that J.R.R. Tolkien and C.S. Lewis influenced each other writings. For the first time we have investigated this issue mathematically by using an original multi–dimensional analysis of linguistic parameters, based on surface deep–language variables and linguistic channels. To set our investigation in the framework of the English Liter-ature, we have considered some novels written by earlier authors, such as C. Dickens, G. Mac-Donald and others. The deep–language variables and the linguistic channels, discussed in the paper, are likely due to writers’ unconscious design and have revealed connections between texts far beyond writers’ awareness. In summary, the capacity of the extended short–term memory required to readers, the universal readability index of texts, the geometrical representation of texts and the fine tuning of linguistic channels within texts – all tools largely discussed in the paper – have revealed strong connections between The Lord of the Rings (Tolkien), The Chronicles of Narnia, The Space Trilogy (Lewis) and novels by MacDonald, therefore in agreement with what scholars of the English Literature say.
Keywords
Alphabetical Languages; Extended Short–Term Memory; Human Communication; Human Mind; Sentences: Mathematical Modeling; Universal Readability Index
Subject
Arts and Humanities, Literature and Literary Theory
Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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