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Journal of Scientific Information Research

Keywords

citation topic, citation pattern, semantic association, allusion citation, poet association mining

Abstract

[Purpose/significance] Establishing literary connections between poets from the perspective of allusion citation is of great significance for exploring information on unknown poets and understanding their works. [Method/process] This study investigates poets' citation patterns from the perspective of allusion citation, integrating both emotional and thematic features. Regarding emotional features, we construct a sentiment recognition model for poetic lines using the Dictionary of Tang Poetry Appreciation, which combines poems with their corresponding critical annotations. The model predicts the sentiment of each poetic line, which is then aligned with the sentiment of the cited allusion to derive citation sentiment. By quantifying the distribution of different citation sentiments for each poet, we identify their citation sentiment types. Regarding thematic features, we employ an author-topic model to extract citation themes, yielding the probability distribution of each poet across themes and generating thematic citation vectors. By combining sentiment types with thematic vectors, we construct poets' citation patterns, upon which community detection is performed based on pattern similarity to form poet groups. Within these groups, information about lesser-known poets can be inferred through the presence of prominent poets. Additionally, we explore the distribution of citation patterns within the same historical period and dominant themes. [Result/conclusion] The findings demonstrate that analyzing allusion citation provides an effective means of uncovering semantic associations among poets and offers auxiliary support for the discovery of works by poets who have not yet received substantial scholarly attention.

First Page

44

Last Page

55

Submission Date

9-Oct-2025

Revision Date

13-Dec-2025

Acceptance Date

12-Jan-2026

Published Date

1-Oct-2026

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Digital Object Identifier (DOI)

10.19809/j.cnki.kjqbyj.2026.04.005

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