dynamic-topic-modeling 1.1.0

Run dynamic topic modeling

dynamic_topic_modeling

Run dynamic topic modeling.

The goal of 'wei_lda_debate' is to build Latent Dirichlet Allocation models based on 'sklearn' and 'gensim' framework, and Dynamic Topic Model(Blei and Lafferty 2006) based on 'gensim' framework. I decide to build a Python package 'dynamic_topic_modeling', so this reposority will be updated and 'wei_lda_debate' is depreciated. The new reposority path is https://github.com/JiaxiangBU/dynamic_topic_modeling.git.

To build this package, I borrow from

1. 'wei_lda_debate'(Wang 2018) to build LDA framework

2. 'dtmvisual'(Svitlana 2019) to build the visualization framework. Moreover, this package seems like a visualiztaion tutorial using jupyter notebook for 'dtmvisual'.

3. LDA based on sklearn

4. LDA based on gensim

5. Dynamic Topic Modeling

6. Data Analysis on Demi Gods and Semi Devils using Dynamic Topic Modeling

Jiaxiang Li. (2020, February 9). JiaxiangBU/dynamic_topic_modeling: dynamic_topic_modeling 1.1.0 (Version v1.1.0). Zenodo. http://doi.org/10.5281/zenodo.3660401

@software{jiaxiang_li_2020_3660401,
  author       = {Jiaxiang Li},
  title        = {{JiaxiangBU/dynamic_topic_modeling: 
                   dynamic_topic_modeling 1.1.0}},
  month        = feb,
  year         = 2020,
  publisher    = {Zenodo},
  version      = {v1.1.0},
  doi          = {10.5281/zenodo.3660401},
  url          = {https://doi.org/10.5281/zenodo.3660401}
}

If you use dynamic_topic_modeling, I would be very grateful if you can add a citation in your published work. By citing dynamic_topic_modeling, beyond acknowledging the work, you contribute to make it more visible and guarantee its growing and sustainability. For citation, please use the BibTex or the citation content.

Install

pip install dynamic_topic_modeling

How to use

1. LDA based on sklearn
2. LDA based on gensim
3. Dynamic Topic Modeling
4. Data Analysis on Demi Gods and Semi Devils using Dynamic Topic Modeling

Jiaxiang Li. (2020, February 9). JiaxiangBU/dynamic_topic_modeling: dynamic_topic_modeling 1.1.0 (Version v1.1.0). Zenodo. http://doi.org/10.5281/zenodo.3660401

@software{jiaxiang_li_2020_3660401,
  author       = {Jiaxiang Li},
  title        = {{JiaxiangBU/dynamic\_topic\_modeling: 
                   dynamic\_topic\_modeling 1.1.0}},
  month        = feb,
  year         = 2020,
  publisher    = {Zenodo},
  version      = {v1.1.0},
  doi          = {10.5281/zenodo.3660401},
  url          = {https://doi.org/10.5281/zenodo.3660401}
}

If you use dynamic_topic_modeling, I would be very grateful if you can add a citation in your published work. By citing dynamic_topic_modeling, beyond acknowledging the work, you contribute to make it more visible and guarantee its growing and sustainability. For citation, please use the BibTex or the citation content.

Code of Conduct

Please note that the dynamic_topic_modeling project is released with a Contributor Code of Conduct.
By contributing to this project, you agree to abide by its terms.

License

Apache License c Jiaxiang Li;Shuyi Wang;Svitlana Galeshchuk

Blei, David M., and John D. Lafferty. 2006. "Dynamic Topic Models." In Machine Learning, Proceedings of the Twenty-Third International Conference (Icml 2006), Pittsburgh, Pennsylvania, Usa, June 25-29, 2006.

Svitlana. 2019. "Dtmvisual: This Package Consists of Functionalities for Dynamic Topic Modelling and Its Visualization." GitHub. 2019. https://github.com/GSukr/dtmvisual.

Wang, Shuyi. 2018. GitHub. 2018. https://github.com/wshuyi/wei_lda_debate.