A COMPARATIVE CORPUS-BASED STUDY OF HUMOUR IN MURDOCH MYSTERIES: ANALYSING SEASON 13 WITH LANCSBOX X AND TROPES
A COMPARATIVE CORPUS-BASED STUDY OF HUMOUR IN MURDOCH MYSTERIES: ANALYSING SEASON 13 WITH LANCSBOX X AND TROPES
Author(s): Zamfira-Maria PetrescuSubject(s): Language and Literature Studies, Literary Texts, Fiction, Applied Linguistics, Novel, Descriptive linguistics
Published by: Шуменски университет »Епископ Константин Преславски«
Keywords: corpus linguistics; humour; software analysis; “Murdoch Mysteries”
Summary/Abstract: In qualitative and quantitative linguistic research, software tools are critical for analysing large text corpora. LancsBox X and Tropes represent two distinct paradigms: the first is rooted in corpus linguistics, while the latter in semantic and discourse analysis. This study evaluates how each program performs when applied to the same dataset: a corpus of 477 jokes or forms of witticism extracted from Murdoch Mysteries Television Series, Season 13. The data comprises 26375 words structured as 477 fragments containing jokes or anecdotes selected from 18 episodes that were scrutinised using the tools LancsBox X (v5.0.3) and Tropes (v8.4, English interface), both applications being assessed according to the following criteria: Functionality and Features, Accessibility, Visual Outputs and Export. The objective of this article is to compare the effectiveness of LancsBox X and Tropes in performing textual analysis for academic and research purposes, particularly focusing on functionality, usability and analytical depth. The concordances and frequency data extracted with the help of these tools illustrate the quantitative and qualitative forms of analysis that are equally significant to corpus linguistics.I dedicated this article to Linguistics students and their mentors, whose interest switched from a literary analysis to corpus linguistics, but they were not trained to use any tools.
Journal: Годишник на Шуменския университет "Епископ Константин Преславски". Факултет по хуманитарни науки
- Issue Year: XXXVI/2025
- Issue No: 2
- Page Range: 490-505
- Page Count: 16
- Language: English
