Text mining and semantic modeling of literary corpora: a machine learning–based study of Indonesian fiction
DOI:
https://doi.org/10.64595/lingtech.v2i1.133Keywords:
affective intensity, Indonesian fiction, machine learning, semantic modeling, text miningAbstract
Background: The large-scale digitization of Indonesian literary works has produced extensive textual corpora that challenge conventional close-reading approaches and call for systematic, data-driven methods capable of capturing thematic, semantic, and affective patterns in fiction.
Objective: This study aims to examine how text mining and semantic modeling can reveal lexical salience, intertextual relations, and narrative emotion in Indonesian fiction across different thematic orientations.
Method: Using a quantitative corpus-based design, the study analyzes 36 Indonesian literary texts published between 1980 and 2022 through TF–IDF–based lexical analysis, document-level semantic embeddings with cosine similarity and clustering, and sentence-level sentiment analysis.
Results: The findings show distinct lexical signatures that differentiate thematic clusters, coherent semantic groupings reflecting intertextual proximity, and sentiment trajectories dominated by neutral-to-negative polarity with strategically placed affective peaks across narrative progression.
Implication: These results demonstrate that computational methods can empirically support literary analysis without displacing interpretive criticism.
Novelty: The study integrates lexical, semantic, and affective modeling within a unified framework for Indonesian fiction, offering a scalable and replicable approach to digital literary studies.
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