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

@article{Jelodar2017LatentDA,
  title={Latent Dirichlet allocation (LDA) and topic modeling: models, applications, a survey},
  author={Hamed Jelodar and Yongli Wang and Chi Yuan and Xia Feng},
  journal={Multimedia Tools and Applications},
  year={2017},
  volume={78},
  pages={15169-15211}
}
  • Hamed Jelodar, Yongli Wang, +1 author Xia Feng
  • Published in
    Multimedia Tools and…
    2017
  • Computer Science
  • 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… CONTINUE READING

    Create an AI-powered research feed to stay up to date with new papers like this posted to ArXiv

    Figures, Tables, and Topics from this paper.

    Citations

    Publications citing this paper.
    SHOWING 1-10 OF 43 CITATIONS

    Temporal Recommendations for Discovering Author Interests

    VIEW 1 EXCERPT
    CITES METHODS

    Statistical Topic Modeling for Urdu Text Articles

    VIEW 2 EXCERPTS
    CITES BACKGROUND

    Review of Latent Dirichlet Allocation Methods Usable in Voice of Customer Analysis

    VIEW 2 EXCERPTS
    CITES BACKGROUND

    Applying Statistical Approach to Topic Analysis for more Comprehensive and Appropriate Modeling

    VIEW 1 EXCERPT
    CITES BACKGROUND

    References

    Publications referenced by this paper.
    SHOWING 1-10 OF 161 REFERENCES

    Locally discriminative topic modeling

    VIEW 13 EXCERPTS
    HIGHLY INFLUENTIAL

    UTOPIAN: User-Driven Topic Modeling Based on Interactive Nonnegative Matrix Factorization

    VIEW 1 EXCERPT