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Latent Dirichlet allocation (LDA) and topic modeling: models, applications, a survey

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Abstract

Topic modeling is one of the most powerful techniques in text mining for data mining, latent data discovery, and finding relationships among data and text documents. Researchers have published many articles in the field of topic modeling and applied in various fields such as software engineering, political science, medical and linguistic science, etc. There are various methods for topic modelling; Latent Dirichlet Allocation (LDA) is one of the most popular in this field. Researchers have proposed various models based on the LDA in topic modeling. According to previous work, this paper will be very useful and valuable for introducing LDA approaches in topic modeling. In this paper, we investigated highly scholarly articles (between 2003 to 2016) related to topic modeling based on LDA to discover the research development, current trends and intellectual structure of topic modeling. In addition, we summarize challenges and introduce famous tools and datasets in topic modeling based on LDA.

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Acknowledgements

This article has been awarded by the National Natural Science Foundation of China (61170035, 61272420, 81674099, 61502233), the Fundamental Research Fund for the Central Universities (30916011328, 30918015103), and Nanjing Science and Technology Development Plan Project (201805036).

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  1. School of Computer Science and Technology, Nanjing University of Science and Technology, Nanjing, 210094, China

    Hamed Jelodar, Yongli Wang, Chi Yuan, Xia Feng, Xiahui Jiang, Yanchao Li & Liang Zhao

  2. China Electronics Technology Cyber Security Co., Ltd, Chengdu, China

    Yongli Wang

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  1. Hamed Jelodar
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Jelodar, H., Wang, Y., Yuan, C. et al. Latent Dirichlet allocation (LDA) and topic modeling: models, applications, a survey. Multimed Tools Appl 78, 15169–15211 (2019). https://doi.org/10.1007/s11042-018-6894-4

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