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Topic Detection Based on Sentence Embeddings and Agglomerative Clustering with Markov Moment

Author

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  • Svetlana S. Bodrunova

    (School of Journalism and Mass Communications, Saint Petersburg State University, 7-9 Universitetskaya embankment, 199034 Saint Petersburg, Russia
    These authors contributed equally to this work.)

  • Andrey V. Orekhov

    (School of Journalism and Mass Communications, Saint Petersburg State University, 7-9 Universitetskaya embankment, 199034 Saint Petersburg, Russia
    These authors contributed equally to this work.)

  • Ivan S. Blekanov

    (School of Journalism and Mass Communications, Saint Petersburg State University, 7-9 Universitetskaya embankment, 199034 Saint Petersburg, Russia
    These authors contributed equally to this work.)

  • Nikolay S. Lyudkevich

    (School of Journalism and Mass Communications, Saint Petersburg State University, 7-9 Universitetskaya embankment, 199034 Saint Petersburg, Russia)

  • Nikita A. Tarasov

    (School of Journalism and Mass Communications, Saint Petersburg State University, 7-9 Universitetskaya embankment, 199034 Saint Petersburg, Russia)

Abstract

The paper is dedicated to solving the problem of optimal text classification in the area of automated detection of typology of texts. In conventional approaches to topicality-based text classification (including topic modeling), the number of clusters is to be set up by the scholar, and the optimal number of clusters, as well as the quality of the model that designates proximity of texts to each other, remain unresolved questions. We propose a novel approach to the automated definition of the optimal number of clusters that also incorporates an assessment of word proximity of texts, combined with text encoding model that is based on the system of sentence embeddings. Our approach combines Universal Sentence Encoder (USE) data pre-processing, agglomerative hierarchical clustering by Ward’s method, and the Markov stopping moment for optimal clustering. The preferred number of clusters is determined based on the “e-2” hypothesis. We set up an experiment on two datasets of real-world labeled data: News20 and BBC. The proposed model is tested against more traditional text representation methods, like bag-of-words and word2vec, to show that it provides a much better-resulting quality than the baseline DBSCAN and OPTICS models with different encoding methods. We use three quality metrics to demonstrate that clustering quality does not drop when the number of clusters grows. Thus, we get close to the convergence of text clustering and text classification.

Suggested Citation

  • Svetlana S. Bodrunova & Andrey V. Orekhov & Ivan S. Blekanov & Nikolay S. Lyudkevich & Nikita A. Tarasov, 2020. "Topic Detection Based on Sentence Embeddings and Agglomerative Clustering with Markov Moment," Future Internet, MDPI, vol. 12(9), pages 1-17, August.
  • Handle: RePEc:gam:jftint:v:12:y:2020:i:9:p:144-:d:404427
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    References listed on IDEAS

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    1. Svetlana S. Bodrunova & Ivan Blekanov & Anna Smoliarova & Anna Litvinenko, 2019. "Beyond Left and Right: Real-World Political Polarization in Twitter Discussions on Inter-Ethnic Conflicts," Media and Communication, Cogitatio Press, vol. 7(3), pages 119-132.
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    Cited by:

    1. Ivan S. Blekanov & Nikita Tarasov & Svetlana S. Bodrunova, 2022. "Transformer-Based Abstractive Summarization for Reddit and Twitter: Single Posts vs. Comment Pools in Three Languages," Future Internet, MDPI, vol. 14(3), pages 1-25, February.
    2. Svetlana S. Bodrunova, 2022. "Editorial for the Special Issue “Selected Papers from the 9th Annual Conference ‘Comparative Media Studies in Today’s World’ (CMSTW’2021)”," Future Internet, MDPI, vol. 14(11), pages 1-3, November.
    3. Andrey V. Orekhov, 2021. "Quasi-Deterministic Processes with Monotonic Trajectories and Unsupervised Machine Learning," Mathematics, MDPI, vol. 9(18), pages 1-26, September.
    4. Ivan Blekanov & Svetlana S. Bodrunova & Askar Akhmetov, 2021. "Detection of Hidden Communities in Twitter Discussions of Varying Volumes," Future Internet, MDPI, vol. 13(11), pages 1-17, November.

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