Mapping Topic Evolution Across Poetic Traditions

TL;DR

This study uses LDA to analyze and compare the evolution of semantic topics in poetry across four languages from 1600 to 1925, revealing both similarities and differences in poetic traditions over time.

Contribution

It introduces a cross-lingual topic modeling approach to trace poetic themes and their temporal dynamics across multiple literary traditions.

Findings

Identified common and divergent poetic topics across languages.

Mapped the temporal evolution of themes to specific literary epochs.

Highlighted differences in poetic trajectories over 300 years.

Abstract

Poetic traditions across languages evolved differently, but we find that certain semantic topics occur in several of them, albeit sometimes with temporal delay, or with diverging trajectories over time. We apply Latent Dirichlet Allocation (LDA) to poetry corpora of four languages, i.e. German (52k poems), English (85k poems), Russian (18k poems), and Czech (80k poems). We align and interpret salient topics, their trend over time (1600--1925 A.D.), showing similarities and disparities across poetic traditions with a few select topics, and use their trajectories over time to pinpoint specific literary epochs.