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A Comparative Textual Analysis of Three Historical Resolutions Using Natural Language Processing: Tracing Institutional Memory, Strategic Evolution, and Ideational Continuity

Author(s)

Xiangyou Wu, Zhiqiang Huang*

Affiliation(s)

Minjiang University, New Huadu Business School, Fuzhou, Fujian, China *Corresponding Author

Abstract

Historical resolutions represent pivotal moments of institutional self-reflection, strategic recalibration, and collective memory consolidation. This study presents a comprehensive, data-driven comparative analysis of three landmark historical resolutions issued by a major socio-political organization over the course of a century. Employing an integrated suite of natural language processing (NLP) techniques—including Latent Dirichlet Allocation (LDA) for thematic modeling, TF-IDF for keyword salience, cosine similarity for inter-textual comparison, dependency parsing for syntactic complexity, sentiment and stance analysis, and named entity co-occurrence networks—we uncover nuanced patterns of continuity and transformation across these foundational documents. Our findings reveal a dynamic evolution in core thematic priorities: from foundational consolidation and internal rectification in the first resolution, through a paradigmatic shift toward modernization and systemic reform in the second, to a forward-looking articulation of long-term civilizational mission and governance maturity in the third. Despite significant contextual and lexical shifts, a deep structural coherence persists, anchored in enduring principles of unity, historical consciousness, and adaptive governance. This research not only illuminates the discursive strategies of institutional longevity but also demonstrates the robust applicability of computational linguistics to the rigorous, objective study of historical political texts.

Keywords

Historical Resolutions; Natural Language Processing; Thematic Modeling; Institutional Memory; Textual Analysis; Discourse Evolution; TF-IDF; Semantic Networks

References

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