Computational thematics: comparing algorithms for clustering the genres of literary fiction

What are the best methods of capturing thematic similarity between literary texts? Knowing the answer to this question would be useful for automatic clustering of book genres, or any other thematic grouping. This paper compares a variety of algorithms for unsupervised learning of thematic similariti...

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Veröffentlicht in:Humanities & social sciences communications Jg. 11; H. 1; S. 438 - 12
Hauptverfasser: Sobchuk, Oleg, Šeļa, Artjoms
Format: Journal Article
Sprache:Englisch
Veröffentlicht: London Springer Nature B.V 01.12.2024
Springer Nature
Schlagworte:
Algorithms
Clustering
Crime fiction
Extraction
Fiction
Genre
Machine learning
Novels
Science fiction & fantasy
Truth
Young adults
ISSN:2662-9992, 2662-9992
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Zusammenfassung:What are the best methods of capturing thematic similarity between literary texts? Knowing the answer to this question would be useful for automatic clustering of book genres, or any other thematic grouping. This paper compares a variety of algorithms for unsupervised learning of thematic similarities between texts, which we call “computational thematics”. These algorithms belong to three steps of analysis: text pre-processing, extraction of text features, and measuring distances between the lists of features. Each of these steps includes a variety of options. We test all the possible combinations of these options. Every combination of algorithms is given a task to cluster a corpus of books belonging to four pre-tagged genres of fiction. This clustering is then validated against the “ground truth” genre labels. Such comparison of algorithms allows us to learn the best and the worst combinations for computational thematic analysis. To illustrate the difference between the best and the worst methods, we then cluster 5000 random novels from the HathiTrust corpus of fiction.
Bibliographie:ObjectType-Article-1
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ISSN:2662-9992
2662-9992
DOI:10.1057/s41599-024-02933-6