Eigenmood Space: Uncertainty-Aware Spectral Graph Analysis of Psychological Patterns in Classical Persian Poetry
Kourosh Shahnazari, Seyed Moein Ayyoubzadeh, Mohammadali Keshtparvar

TL;DR
This paper introduces an uncertainty-aware spectral graph analysis framework for studying psychological patterns in classical Persian poetry, enabling scalable, interpretable, and cautious poet-level insights from large verse datasets.
Contribution
It presents a novel computational approach combining confidence-weighted evidence, divergence measures, and spectral embeddings to analyze poetic psychology with uncertainty quantification.
Findings
22.2% of verses are abstained, highlighting the importance of uncertainty.
The framework effectively captures poetic individuality through divergence measures.
Spectral embeddings reveal meaningful psychological axes in poetry.
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 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,…
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Taxonomy
TopicsArtificial Intelligence in Games · Aesthetic Perception and Analysis · Sentiment Analysis and Opinion Mining
