Data-Driven Feminist Text Analysis: Exploring the Significance of Computational Methods and Digital Humanities Tools in Literary and Cultural Studies
The role of feminist text analysis in literary and cultural studies, with a particular focus on the use of data and code-based tools to support this approach. Drawing on established feminist theories and practices of text analysis, we argue that feminist text analysis is a crucial lens for understanding how gender and power dynamics shape the production and reception of literature and other cultural artifacts.
We explore the ways in which computational methods and digital humanities tools can support feminist text analysis, including through the use of text mining, machine learning (for example: machine learning algorithms can be trained to identify and classify gendered language and stereotypes in texts, which can then be used to quantify and analyze patterns of gender bias and discrimination. This can enable feminist text analysts to more efficiently and effectively identify and critique problematic representations of gender in literature and other cultural artifacts) and other data-driven approaches.
Consider the challenges and limitations of these tools is also very crucial, including the potential for bias and the need for critical awareness of their limitations. To support this argument, examples of feminist text analyses that have successfully navigated these challenges, including studies on the representation of gender in children’s books, the use of the word “hysterical” on Twitter, and the gendering of job titles in academia. These examples demonstrate the potential of feminist text analysis to uncover patterns of gender bias and inequality, and to contribute to the promotion of gender equality and social justice. Ultimately, we argue that feminist text analysis is an essential approach to literary and cultural studies that can help us to create more inclusive and equitable representations of gender in our culture.