JOURNAL ARTICLE

A multidimensional and digital humanistic analysis of style in Amy Tan's novels.

  • Published In: Digital Scholarship in the Humanities, 2023, v. 38, n. 3. P. 1281 1 of 3

  • Database: Academic Search Ultimate 2 of 3

  • Authored By: Tian, Junwu; Liu, Shuyue 3 of 3

Abstract

This article focuses on a multidimensional stylistic analysis of Amy Tan's six novels using Douglas Biber's multidimensional model (MDA) of lexico-grammatical features, facilitated by the Multidimensional Analysis Tagger (MAT) and statistical software R. The study examines Tan's novels along six dimensions—Involved versus Informational Production; Narrative versus Non-Narrative Concerns; Explicit versus Situation-Dependent Reference; Overt Expression of Persuasion; Abstract versus Non-Abstract Information; and On-Line Informational Elaboration—to identify stylistic variations and test correlations between dimensions. Findings reveal distinct stylistic profiles among the novels, such as The Kitchen God's Wife's high interactivity and involvement, and Saving Fish From Drowning's informational density and explicit referencing, with a statistically significant positive correlation found between dimensions 3 (explicit versus situation-dependent reference) and 5 (abstract versus nonabstract information). The study highlights the value of combining computational and qualitative methods in literary stylistics and suggests that stylistic qualities in Tan's work merit attention alongside cultural and ethnic interpretations.

Additional Information

  • Source:Digital Scholarship in the Humanities. 2023/09, Vol. 38, Issue 3, p1281
  • Document Type:Article
  • Subject Area:Literature and Writing
  • Publication Date:2023
  • ISSN:2055-768X
  • DOI:10.1093/llc/fqac096
  • Accession Number:171389411
  • Copyright Statement:Copyright of Digital Scholarship in the Humanities is the property of Oxford University Press / USA and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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