<![CDATA[This study develops a comparative quantitative reading of three major Bengali literary signatures: Kazi Nazrul Islam, Rabindranath Tagore, and Humayun Ahmed. The analysis uses datasets the researcher created through deep analysis of lexical, thematic, spatial, stylistic, work-level, and series-level measurements. Because the datasets use different units, the cross-author comparison is based primarily on within-author ranks, normalized concentration measures, and nonparametric statistics rather than pooled raw counts. Three novel relationships emerge. First, a harmonized set of 13 shared motifs shows a strong rank-order alignment between Nazrul and Tagore (Spearman ρ = 0.775, p = 0.0019, Benjamini–Hochberg q = 0.0056), but not between Humayun and either poet, suggesting a shared poetic motif backbone that later prose reorganizes. Second, Humayun’s distinctive motif profile shifts toward rain, moon, cloud, bird, and night, while his recurring place profile is unusually even (normalized Shannon entropy = 0.990), anchored in Dhaka, tea stalls, rooftops, rickshaws, rented flats, hospitals, and university space. Third, within 35 dated Nazrul entries, age is negatively related to revolutionary intensity (Spearman ρ = −0.539, q = 0.0014; Theil–Sen slope = −0.429 index points/year, 95% CI [−0.714, −0.167]) and positively related to romantic intensity (ρ = 0.573, q = 0.0013; slope = 0.400, 95% CI [0.200, 0.667]). A sensitivity analysis of the first 17 sequential collections additionally detects rising spiritual intensity with age. Tagore’s ten-work profile yields a first principal component that explains 61.8% of the variance and separates lyric-contemplative works from social-narrative works. The paper therefore proposes a data-derived model of Bengali literary modernity in which common motifs persist, but their rank, spatial anchoring, and genre function are reorganized across authors and historical moments. Results are exploratory because the inputs include analyst-coded and estimated measurements rather than a uniform raw-text corpus.]]>
