Computational approaches to semantic frame modeling: frequency and statistical analysis of nonverbal experience in war fiction

This study applies computational approaches to semantic frame modeling by integrating statistical tests (ANOVA, Tukey's Honest Significant Difference, Chi-Square) within computer-based discourse analysis to examine verbalized nonverbal experience in modern war fiction.The research focuses on the semantic structuring or categorization of nonverbal communication/behavior, referred to as constants of nonverbal experience (CNE), which are systematically categorized within a semantic frame.War fiction, beyond depicting the horrors of war, is a rich source of nonverbal experience, encompassing themes of memory, relationships, and human resilience.This study quantitatively models the CNE semantic frame, identifying its four primary slots: Gesture, Posture, Face, and Voice.By exploring the most frequently occurring words across these categories, the research uncovers how nonverbal elements shape narrative meaning at both surface and deeper semantic levels.The statistical findings underscore the cohesion between conceptual and semantic structures, highlighting the stability and consistency of CNE distributions across war fiction corpora.The integration of Voyant Tools and R programming for data processing enhances the accuracy and interpretability of frequency and statistical analysis, reinforcing the CNE semantic frame as a structured linguistic and conceptual phenomenon.By combining quantitative linguistic analysis with computational approach, this research contributes to a deeper understanding of nonverbal experience in war narrative, demonstrating how statistical insights enhance the study of meaning construction in fictional texts.