Eigenmood Space: Uncertainty-Aware Spectral Graph Analysis of Psychological Patterns in Classical Persian Poetry

2026-02-18

AI Summary

This study addresses the challenge posed by the heavy reliance on metaphor in classical Persian poetry, which impedes large-scale, reproducible analysis of psychological patterns. To overcome this, we propose the first uncertainty-aware computational framework that automatically annotates poetic lines with psychological concepts via multi-label classification. By incorporating confidence-weighted aggregation and an abstention mechanism, the approach constructs a poet–concept matrix, from which interpretable Eigenmood embeddings are derived through Laplacian spectral decomposition of a co-occurrence graph. This method uniquely integrates uncertainty modeling, abstention strategies, and spectral graph embedding to establish an auditable digital humanities pipeline. Evaluated on 61,573 poetic lines, the framework abstained on 22.2% of samples due to low confidence, underscoring the necessity of explicit uncertainty handling. The resulting Eigenmood axes enable meaningful retrieval of representative verses, supporting scalable distant reading as well as close reading.

Technology Category

• Reasoning under Uncertainty: Uncertainty Representations
• Application Domains: Humanities & Computational Social Science
• Natural Language Processing: Sentiment Analysis, Stylistic Analysis, and Argument Mining

Application Category

• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods
• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs

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 Eigenmood embedding through Laplacian spectral decomposition. On a corpus of 61{,}573 verses across 10 poets, 22.2\% of verses are abstained, underscoring the analytical importance of uncertainty. We further report sensitivity analysis under confidence thresholding, selection-bias diagnostics that treat abstention as a category, and a distant-to-close workflow that retrieves verse-level exemplars along Eigenmood axes. The resulting framework supports scalable, auditable digital-humanities analysis while preserving interpretive caution by propagating uncertainty from verse-level evidence to poet-level inference.

Problem

• Classical Persian poetry
• psychological patterns
• uncertainty
• computational analysis
• digital humanities

Innovation

• Eigenmood embedding
• uncertainty-aware annotation
• spectral graph analysis
• confidence-weighted co-occurrence
• computational digital humanities