| Author: | Fu, Ming Clara |
| Title: | A corpus-based comparative linguistic analysis on Poe's and Doyle's detective fictions |
| Advisors: | Lam, Marvin (ENGL) |
| Degree: | M.A. |
| Year: | 2016 |
| Subject: | Detective and mystery stories, English -- History and criticism. Hong Kong Polytechnic University -- Dissertations |
| Department: | Department of English |
| Pages: | 53 pages |
| Language: | English |
| Abstract: | Poe and Doyle have been two significant authors in the genre of detective fictions. Previous study has demonstrated the salient features in terms of language and gender in both Poe's and Doyle's work. There are debates on the females' position in their detective fictions. However, traditional literature analysis normally has not presented enough evidence of the statement. Since linguistic analysis and corpus-based comparative analysis has been proved to be effective and efficient in literature analysis, this paper aims at conducting a corpus-based comparative linguistic analysis on Poe's and Doyle's detective fictions. The specialized corpus is generated by five detective fictions from Poe and five from Doyle. Halliday's concept of metafunctions has been adopted to study the use of 'not', modality and personal pronouns related to genders. Examples and data have been presented to further illustrate the connection between linguistic aspects and gender attitude in those selected ten detective fictions. The research is to identify the salient linguistic features in the selected detective fictions, analyze the two authors writing styles in terms of female and male characters, and discuss the connection between linguistic features and previous literature analysis. The findings would support traditional literature analysis on Poe's and Doyle's fictions, as well as enlighten future study. |
| Rights: | All rights reserved |
| Access: | restricted access |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| b29170874.pdf | For All Users (off-campus access for PolyU Staff & Students only) | 364.07 kB | Adobe PDF | View/Open |
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