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PSLDA: a novel supervised pseudo document-based topic model for short texts

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Abstract

Various kinds of online social media applications such as Twitter and Weibo, have brought a huge volume of short texts. However, mining semantic topics from short texts efficiently is still a challenging problem because of the sparseness of word-occurrence and the diversity of topics. To address the above problems, we propose a novel supervised pseudo-document-based maximum entropy discrimination latent Dirichlet allocation model (PSLDA for short). Specifically, we first assume that short texts are generated from the normal size latent pseudo documents, and the topic distributions are sampled from the pseudo documents. In this way, the model will reduce the sparseness of word-occurrence and the diversity of topics because it implicitly aggregates short texts to longer and higher-level pseudo documents. To make full use of labeled information in training data, we introduce labels into the model, and further propose a supervised topic model to learn the reasonable distribution of topics. Extensive experiments demonstrate that our proposed method achieves better performance compared with some state-of-the-art methods.

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Author information

Authors and Affiliations

  1. School of Economics and Management, Beihang Univeristy, Beijing, 100191, China

    Mingtao Sun & Guozhu Jia

  2. School of Computer Science, Beihang University, Beijing, 100191, China

    Xiaowei Zhao, Jian Jing & Deqing Wang

  3. School of Instrumentation and Optoelectronic Engineering, Beihang University, Beijing, 100191, China

    Jingjing Lin

Authors
  1. Mingtao Sun
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  2. Xiaowei Zhao
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  3. Jingjing Lin
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  4. Jian Jing
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  5. Deqing Wang
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  6. Guozhu Jia
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Corresponding author

Correspondence to Guozhu Jia.

Additional information

Mingtao Sun is a PhD candidate in School of Economics and Management, Beihang University, China. His research interests include Big Data processing and Education Administration.

Xiaowei Zhao is currently pursuing the PhD degree in Computer Science with Beihang University, China. Her main research interests include transfer learning and sentiment analysis.

Jingjing Lin is currently a senior student at the School of Instrumentation and Optoelectronic Engineering, Beihang University, China. Her research interests include text classification, natural language inference, and sentiment analysis.

Jian Jing received the MS degree in the Engineering of Computer Techonlogy from the Beihang University, China in 2021. His research interests include knowledge reasoning, algorithms and big data processing.

Deqing Wang received the PhD degree in computer science from Beihang University, China in 2013. He is currently an Associate Professor with the School of Computer Science and the Deputy Chief Engineer with the National Engineering Research Center for Science Technology Resources Sharing and Service, Beihang University, China. His research focuses on text categorization and data mining for software engineering and machine learning.

Guozhu Jia received the PhD degree from Aalborg University, Denmark. He is currently a Professor of School of Economics and Management, Beihang University, China and a member of Expert Committee of China Manufacturing Servitization Alliance. He is also a director of China Innovation Method Society.

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Sun, M., Zhao, X., Lin, J. et al. PSLDA: a novel supervised pseudo document-based topic model for short texts. Front. Comput. Sci. 16, 166350 (2022). https://doi.org/10.1007/s11704-021-0606-3

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  • DOI: https://doi.org/10.1007/s11704-021-0606-3

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