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[2602.16959] Eigenmood Space: Uncertainty-Aware Spectral Graph Analysis of Psychological Patterns in Classical Persian Poetry
Summary
This article presents a framework for analyzing psychological patterns in Classical Persian poetry using uncertainty-aware spectral graph analysis, enhancing interpretive caution and scalability in digital humanities research.
Why It Matters
The study addresses the challenge of analyzing complex emotional expressions in poetry through computational methods. By incorporating uncertainty in its analysis, it provides a more nuanced understanding of poetic individuality and supports scalable research in digital humanities, which is crucial for preserving cultural heritage.
Key Takeaways
- Introduces an uncertainty-aware framework for analyzing psychological patterns in poetry.
- Utilizes a Poet × Concept matrix for poet-level psychological analysis.
- Emphasizes the importance of uncertainty in literary analysis with a significant abstention rate.
- Employs spectral graph analysis to quantify poetic individuality.
- Supports scalable digital humanities research while maintaining interpretive caution.
Computer Science > Computation and Language arXiv:2602.16959 (cs) [Submitted on 18 Feb 2026] Title:Eigenmood Space: Uncertainty-Aware Spectral Graph Analysis of Psychological Patterns in Classical Persian Poetry Authors:Kourosh Shahnazari, Seyed Moein Ayyoubzadeh, Mohammadali Keshtparvar View a PDF of the paper titled Eigenmood Space: Uncertainty-Aware Spectral Graph Analysis of Psychological Patterns in Classical Persian Poetry, by Kourosh Shahnazari and 2 other authors View PDF HTML (experimental) Abstract:Classical Persian poetry is a historically sustained archive in which affective life is expressed through metaphor, intertextual convention, and rhetorical indirection. These properties make close reading indispensable while limiting reproducible comparison at scale. We present an uncertainty-aware computational framework for poet-level psychological analysis based on large-scale automatic multi-label annotation. Each verse is associated with a set of psychological concepts, per-label confidence scores, and an abstention flag that signals insufficient evidence. We aggregate confidence-weighted evidence into a Poet $\times$ Concept matrix, interpret each poet as a probability distribution over concepts, and quantify poetic individuality as divergence from a corpus baseline using Jensen--Shannon divergence and Kullback--Leibler divergence. To capture relational structure beyond marginals, we build a confidence-weighted co-occurrence graph over concepts and define an Eige...
