AI-Driven Deep Learning Discourse Analysis: Class Narrative and the Shifting Subjectivity of Women in the Film Adaptations of Jane Eyre

Abstract

Gender representation in cinema has long reflected and reproduced broader societal inequalities, yet the mechanisms through which canonical literary adaptations negotiate female subjectivity across historical moments remain underexplored by computational methods. This study presents an AI-driven deep learning discourse analysis of female representation across film adaptations of Charlotte Brontë's Jane Eyre (1847), one of the most frequently adapted texts in the English literary tradition. We propose AttentionFusionNet, a novel deep learning architecture combining multi-head self-attention over tabular gender feature tokens with a residual multilayer perceptron (MLP) branch, fused through a gated mechanism and trained under a multi-task learning objective that jointly predicts Bechdel Test scores operationalised as a quantitative measure of female narrative subjectivity and Academy Award recognition. Based on information on The Movie Database, Bechdel Test Movie List, and historical data on the Academy Awards, the model combines cast and crew gender representation scores, commercial performance indicators and prestige indicators to generate interpretable, data driven evaluations of gender dynamics in the cinema industry. Synthetic Minority Oversampling Technique (SMOTE) and class-weighted loss functions are used to overcome ordinal imbalance in classes of Bechdel scoring schema of four classes. The analysis of explainability through GradientExplainer-based SHapley Additive exPlanations (SHAP) attribution demonstrates that the female representation of the cast and crew members is the major predictive signal, and the commercial and prestige indicators represent the secondary predictive ability, especially in intermediate Bechdel classes that represent partial narrative conformity. The results are discussed in the discourse analytical convention of feminist studies of adaptation with references to the theoretical frameworks of Mulvey, de Lauretis, Fairclough and Bourdieu to place the computational outputs into the greater context of cinematic gender construction politics. The publication provides the first methodologically comprehensive computational account of gendered literary adaptation at the scale of single canonical text, an integrated framework of modellable results, interpretable feature analysis, and evidence-based implications to feminist film theory, computation humanities, and production that is inclusive of all.