This study presents a hybrid topic modelling framework for computational literary analysis that integrates Latent Dirichlet Allocation (LDA) with sparse Partial Least Squares Discriminant Analysis (sPLS-DA) to model thematic structure and longitudinal dynamics in narrative poetry. As a case study, we analyse Evgenij Onegin-Aleksandr S. Pushkin's novel in verse-using an Italian translation, testing whether unsupervised and supervised lexical structures converge in a small-corpus setting. The poetic text is segmented into thirty-five documents of lemmatised content words, from which five stable and interpretable topics emerge. To address small-corpus instability, a multi-seed consensus protocol is adopted. Using sPLS-DA as a supervised probe enhances interpretability by identifying lexical markers that refine each theme. Narrative hubs-groups of contiguous stanzas marking key episodes-extend the bag-of-words approach to the narrative level, revealing how thematic mixtures align with the poem's emotional and structural arc. Rather than replacing traditional literary interpretation, the proposed framework offers a computational form of close reading, illustrating how lightweight probabilistic models can yield reproducible thematic maps of complex poetic narratives, even when stylistic features such as metre, phonology, or native morphology are abstracted away. Despite relying on a single lemmatised translation, the approach provides a transparent methodological template applicable to other high-density literary texts in comparative studies.
Figures & tables
ID
Hub label
Start (ch., stanza)
End (ch., stanza)
H1
A typical day in Evgenij’s life
(1, XV)
(1, XXVIII)
H2
The friendship between Evgenij and the Poet
(1, XLV)
(1, L)
H3
Tat’jana’s introduction
(2, XXIV)
(2, XXIX)
H4
Tat’jana falls in love
(3, VII)
(3, XXI)
H5
Tat’jana’s letter
(3, XXXI)
(3, XXXI)
H6
Evgenij’s reply
(4, XI)
(4, XVII)
Table 1: Selected list of narrative hubs in Evgenij Onegin , with corresponding chapter (ch.) and stanza ranges.
Topic
Label
nk
γˉk
T 1
Natural & lyrical imagery
7
0.194
T 2
Individual expectations & aspirations
8
0.196
T 3
Lyrical–sentimental effusion
10
0.241
T 4
Social & mundane milieu
5
0.176
T 5
Feelings & social conventions
5
0.193
Table 2: Topics with their interpretive label, per-topic dominant-document count nk , and mean prevalence γˉk .
Figure 1: Top 20 terms per topic (prevalence-weighted). Panel area per topic is proportional to its mean prevalence; tile area per lemma is proportional to its within-topic weight. Italian lemmas (left); English glosses (right).
Figure 2: Narrative map across documents. Grey bands mark the narrative hubs described in Table 1 .
Topic
acc
bacc
acc0
bacc0
Natural & lyrical imagery
0.88
0.81
0.72
0.55
Individual expectations & aspirations
0.76
0.60
0.69
0.53
Lyrical–sentimental effusion
0.86
0.81
0.58
0.47
Social & mundane milieu
0.89
0.68
0.79
0.55
Feelings & social conventions
0.86
0.58
0.76
0.48
Table 3: Cross-validated performance of sPLS–DA OvR classifiers by topic. Columns: mean accuracy ( acc ), balanced accuracy ( bacc ), and stratified random baselines ( acc0 , bacc0 ).
Figure 3: Cross-over heatmap between sPLS–DA core-exclusive lemmas and LDA top lemmas. Diagonal values indicate within-theme overlap; off-diagonal values indicate cross-theme overlap.
Figure 4: Chord diagram showing interrelations between the five main themes (bottom) and their associated subthemes (top).
Theme / Subtheme
Lemmas (IT)
Lyrical–sentimental effusion
Art and culture
voce, pensiero
Characters
Evgenij
Countryside domestic life
casa, fuoco ‡
Dreams and ideals
anima, destino, giorno, mondo, sogno, speranza, tempo
Table 4: Tat’jana’s letter — lexical hub card. sPLS–DA lemmas are denoted by † or ‡ ; † , novel lemma (not in LDA top lists); ‡ , porous lemma (appearing among the top terms of another topic).