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Pseudo-document simulation for comparing LDA, GSDMM and GPM topic models on short and sparse text using Twitter data

Author

Listed:
  • Christoph Weisser

    (Georg-August-Universität Göttingen
    Campus-Institut Data Science (CIDAS))

  • Christoph Gerloff

    (Georg-August-Universität Göttingen)

  • Anton Thielmann

    (Georg-August-Universität Göttingen)

  • Andre Python

    (Zhejiang University)

  • Arik Reuter

    (Georg-August-Universität Göttingen)

  • Thomas Kneib

    (Georg-August-Universität Göttingen
    Campus-Institut Data Science (CIDAS))

  • Benjamin Säfken

    (Clausthal University of Technology)

Abstract

Topic models are a useful and popular method to find latent topics of documents. However, the short and sparse texts in social media micro-blogs such as Twitter are challenging for the most commonly used Latent Dirichlet Allocation (LDA) topic model. We compare the performance of the standard LDA topic model with the Gibbs Sampler Dirichlet Multinomial Model (GSDMM) and the Gamma Poisson Mixture Model (GPM), which are specifically designed for sparse data. To compare the performance of the three models, we propose the simulation of pseudo-documents as a novel evaluation method. In a case study with short and sparse text, the models are evaluated on tweets filtered by keywords relating to the Covid-19 pandemic. We find that standard coherence scores that are often used for the evaluation of topic models perform poorly as an evaluation metric. The results of our simulation-based approach suggest that the GSDMM and GPM topic models may generate better topics than the standard LDA model.

Suggested Citation

  • Christoph Weisser & Christoph Gerloff & Anton Thielmann & Andre Python & Arik Reuter & Thomas Kneib & Benjamin Säfken, 2023. "Pseudo-document simulation for comparing LDA, GSDMM and GPM topic models on short and sparse text using Twitter data," Computational Statistics, Springer, vol. 38(2), pages 647-674, June.
    • Handle: RePEc:spr:compst:v:38:y:2023:i:2:d:10.1007_s00180-022-01246-z
    DOI: 10.1007/s00180-022-01246-z
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    References listed on IDEAS

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    1. Jocelyn Mazarura & Alta de Waal & Pieter de Villiers, 2020. "A Gamma-Poisson Mixture Topic Model for Short Text," Mathematical Problems in Engineering, Hindawi, vol. 2020, pages 1-17, April.
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    Cited by:

    1. Li, Yao-Tai & Chen, Man-Lin & Lee, Hsuan-Wei, 2024. "Health communication on social media at the early stage of the pandemic: Examining health professionals’ COVID-19 related tweets," Social Science & Medicine, Elsevier, vol. 347(C).

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