Year: 2024
Type: article
Abstract: The paper uses corpus analysis as the empirical basis for aiding in / complementing the characterisation of the Brontës’ best-known Byronic Heroes – Charlotte Brontë’s Mr. Rochester from Jane Eyre, and Emily Brontë’s Heathcliff from Wuthering Heights. The primary aim of the paper is to determine how Mr. Rochester’s and Heathcliff’s speech reflect their Byronic personalities, exploring both the parallels that can be drawn between them and what makes them distinctly different. The corpus analysis is based on the corpora of character speech, and the Sketch Engine corpus tool is used to process the data and perform detailed analyses such as n-grams, which identify common sequences of words, and word frequencies, which highlight the most frequently used words by each character. Additionally, keywords and multi-word expressions are examined to uncover unique and defining linguistic patterns. The results are first interpreted and discussed separately for each character, highlighting their individual linguistic features and unique attributes. Following this, a comparative analysis is conducted to identify the similarities and differences between the two characters based on the corpus data. The discussed features show that Heathcliff’s speech reflects his feelings of inadequacy, brutality, competition and obsession with other people, while it is evident in the case of Mr Rochester that he is undyingly devoted to the woman he loves, while also being tortured by his past. Each of these analytical methods exposes interesting differences in their speech, illustrating how both Mr. Rochester and Heathcliff embody the dark and brooding traits of Byronic Heroes in distinct ways, offering fresh insights into the enduring legacy of these characters.
Source: Journal for Foreign Languages
Authors: Petra Zajc, Monika Kavalir
Institution: University of Ljubljana
Language: English
Topic: Ayn Rand and Brontë studies
Subfield: Economics and Econometrics
Domain: Social Sciences
Open Access status: diamond
OpenAlex ID: W4405723311