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This study presents a comparative analysis of two semantic text visualization tools—Semograph and InfraNodus—applied to World War II interview research focusing on enemy representation. Visualization of semantic networks provides unique insights into narrative structures that would remain obscured in traditional qualitative analysis, revealing non-obvious patterns in how enemies are conceptualized across different emotional, spatial, and operational dimensions. Using a veteran's narrative about military operations, the research examines how each tool constructs and visualizes the enemy image through different methodological approaches. Semograph, employing expert-driven semantic field identification, revealed the centrality of the “Enemies” field (26.64%) with strong connections to spatial localization and military activities, while demonstrating an unexpected prevalence of positive over negative emotional components. InfraNodus, utilizing automated network analysis, identified the concept “German” as the central node with multiple significant connections across thematic clusters, highlighting the multidimensionality of enemy representation. The comparative analysis demonstrates that each tool offers complementary insights: Semograph excels in revealing emotional-evaluative aspects through conceptual categorization, while InfraNodus more effectively captures spatial-temporal and discursive dimensions through lexical analysis. This methodological triangulation enhances understanding of war narrative structures and provides practical recommendations for applying semantic visualization tools in historical memory research.

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Semantic Analysis of Enemy Representations in World War II Narratives: Comparing Text Visualization Methodologies

  • 会议论文

  • 发表日期：

    2026年

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  • Karina Strebkova

摘要

This study presents a comparative analysis of two semantic text visualization tools—Semograph and InfraNodus—applied to World War II interview research focusing on enemy representation. Visualization of semantic networks provides unique insights into narrative structures that would remain obscured in traditional qualitative analysis, revealing non-obvious patterns in how enemies are conceptualized across different emotional, spatial, and operational dimensions. Using a veteran's narrative about military operations, the research examines how each tool constructs and visualizes the enemy image through different methodological approaches. Semograph, employing expert-driven semantic field identification, revealed the centrality of the “Enemies” field (26.64%) with strong connections to spatial localization and military activities, while demonstrating an unexpected prevalence of positive over negative emotional components. InfraNodus, utilizing automated network analysis, identified the concept “German” as the central node with multiple significant connections across thematic clusters, highlighting the multidimensionality of enemy representation. The comparative analysis demonstrates that each tool offers complementary insights: Semograph excels in revealing emotional-evaluative aspects through conceptual categorization, while InfraNodus more effectively captures spatial-temporal and discursive dimensions through lexical analysis. This methodological triangulation enhances understanding of war narrative structures and provides practical recommendations for applying semantic visualization tools in historical memory research.