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

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Listed:
  • Oleg Sobchuk

    (Max Planck Institute for Evolutionary Anthropology)

  • Artjoms Šeļa

    (Polish Academy of Sciences)

Abstract

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.

Suggested Citation

  • Oleg Sobchuk & Artjoms Šeļa, 2024. "Computational thematics: comparing algorithms for clustering the genres of literary fiction," Palgrave Communications, Palgrave Macmillan, vol. 11(1), pages 1-12, December.
    • Handle: RePEc:pal:palcom:v:11:y:2024:i:1:d:10.1057_s41599-024-02933-6
    DOI: 10.1057/s41599-024-02933-6
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    1. Nico Neureiter & Peter Ranacher & Nour Efrat-Kowalsky & Gereon A. Kaiping & Robert Weibel & Paul Widmer & Remco R. Bouckaert, 2022. "Detecting contact in language trees: a Bayesian phylogenetic model with horizontal transfer," Palgrave Communications, Palgrave Macmillan, vol. 9(1), pages 1-14, December.
    2. Stefano Sbalchiero & Maciej Eder, 2020. "Topic modeling, long texts and the best number of topics. Some Problems and solutions," Quality & Quantity: International Journal of Methodology, Springer, vol. 54(4), pages 1095-1108, August.
    3. Lu Liu & Nima Dehmamy & Jillian Chown & C. Lee Giles & Dashun Wang, 2021. "Understanding the onset of hot streaks across artistic, cultural, and scientific careers," Nature Communications, Nature, vol. 12(1), pages 1-10, December.
    4. Lawrence Hubert & Phipps Arabie, 1985. "Comparing partitions," Journal of Classification, Springer;The Classification Society, vol. 2(1), pages 193-218, December.
    5. Efstathios Stamatatos, 2009. "A survey of modern authorship attribution methods," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 60(3), pages 538-556, March.
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