Construction and Validation of a Multidimensional Tragedy Evaluation Model A Computational Narratology Approach

摘要

Quantitative assessment of tragic aesthetics has remained methodologically fragmented, hindering systematic cross-work and cross-tradition comparisons in literary studies.To address this,we constructed and validated the Multidimensional Tragedy Evaluation Model (MDTEM),a computational framework that operationalizes tragic intensity along four core dimensions plus a redemption coefficient. Drawing on a curated corpus of 39 canonical tragedies (20 Greek classical,19 modern Western),we decomposed each work into discrete narrative nodes with sentiment-coded emotional values using the NRC Emotion Lexicon combined with expert Delphi calibration.We then calibrated MDTEM hyperparameters using ordinary least squares and aggregated node-level values via a peak-end rule informed by Kahneman’s cognitive-psychological theory.The model achieved a mean absolute error (MAE)of 3.18,a root mean squared error (RMSE)of 3.92,and a Pearson correlation of r =0.978 between predicted and expert-rated Tragedy Index (TI),with tier classification accuracy reaching 97.4% (38/39 correct).Cross-tradition comparison revealed that Greek classical tragedies exhibited significantly higher TI (Mdn =78.6)than modern Western tragedies (Mdn =67.4;Mann-Whitney U =88.5,p =0.021, r =0.36),driven by higher Scope (S)and lower Redemption (R)coefficients in the Greek corpus.An AI assisted refactoring case study on Romeo and Juliet demonstrated that the model can serve as both a “compass” and“validator” for targeted aesthetic manipulation, successfully shifting the tragedy level from T1 (Extreme,83.4)to T3 (Sorrowful,54.7)with expert agreement.MDTEM thus provides a reproducible,mathematically grounded framework for cross-tradition tragedy research and offers a structured pathway for AI-assisted literary analysis and generation,translating qualitative notions of “cosmic fate” versus“psychological interiority” into measurable aesthetic regularities.