Emotion-based Hierarchical Clustering of Romanian Poetry

Mihaiela LUPEA, Anamaria BRICIU, Elena BOSTENARU

Abstract

Emotions play a central role in both writing and understanding literary works, and poetry is a genre rich in emotional content, vivid imagery and abstract language. This paper proposes a clustering-based approach to unsupervisedly mine emotional patterns in Mihai Eminescu’s poetry. Lexicon-based emotion features are used for the clustering algorithm. Resulting clusters are assessed with regard to manually added characteristics of poems in the form of literary themes. There is a partial overlap between affective and thematic content, consistent with literary evaluations of the same works. Computational approaches have the advantage of being objective and replicable, with unsupervised techniques such as clustering representing a valuable tool in the exploration of literary works. Nonetheless, no specific emotional patterns, as determined by the proposed method, can be fully associated with particular literary themes.

Keywords

Emotion analysis, Unsupervised learning, Hierarchical clustering, Poetry.

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