Metadata-Version: 2.1
Name: dynamic_topic_modeling
Version: 1.1.0
Summary: Run dynamic topic modeling
Home-page: https://github.com/JiaxiangBU/dynamic_topic_modeling
Author: Jiaxiang Li and Shuyi Wang and Svitlana Galeshchuk
Author-email: alex.lijiaxiang@foxmail.com
License: Apache Software License 2.0
Description: 
        # dynamic_topic_modeling
        
        > Run dynamic topic modeling.
        
        
        <!-- README.md is generated from README.Rmd. Please edit that file -->
        
        
        <!-- badges: start -->
        
        [![PyPI
        version](https://badge.fury.io/py/dynamic-topic-modeling.svg)](https://badge.fury.io/py/dynamic-topic-modeling)
        [![DOI](https://zenodo.org/badge/238671296.svg)](https://zenodo.org/badge/latestdoi/238671296)
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        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'.
        
        
        1.  [LDA based on
            sklearn](https://nbviewer.jupyter.org/urls/jiaxiangbu.github.io/dynamic_topic_modeling/sklearn-lda.ipynb)
        2.  [LDA based on
            gensim](https://nbviewer.jupyter.org/urls/jiaxiangbu.github.io/dynamic_topic_modeling/gensim-lda.ipynb)
        3.  [Dynamic Topic
            Modeling](https://nbviewer.jupyter.org/urls/jiaxiangbu.github.io/dynamic_topic_modeling/dtm.ipynb)
        4.  [Data Analysis on Demi Gods and Semi Devils using Dynamic Topic
            Modeling](https://nbviewer.jupyter.org/urls/jiaxiangbu.github.io/dynamic_topic_modeling/demo.ipynb)
        
        
        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](https://nbviewer.jupyter.org/urls/jiaxiangbu.github.io/dynamic_topic_modeling/sklearn-lda.ipynb)
        2.  [LDA based on
            gensim](https://nbviewer.jupyter.org/urls/jiaxiangbu.github.io/dynamic_topic_modeling/gensim-lda.ipynb)
        3.  [Dynamic Topic
            Modeling](https://nbviewer.jupyter.org/urls/jiaxiangbu.github.io/dynamic_topic_modeling/dtm.ipynb)
        4.  [Data Analysis on Demi Gods and Semi Devils using Dynamic Topic
            Modeling](https://nbviewer.jupyter.org/urls/jiaxiangbu.github.io/dynamic_topic_modeling/demo.ipynb)
        
        
        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.
        
        
        <h4 align="center">
        
        **Code of Conduct**
        
        </h4>
        
        <h6 align="center">
        
        Please note that the `dynamic_topic_modeling` project is released with a
        [Contributor Code of
        Conduct](https://github.com/JiaxiangBU/dynamic_topic_modeling/blob/master/CODE_OF_CONDUCT.md).<br>By
        contributing to this project, you agree to abide by its terms.
        
        </h6>
        
        <h4 align="center">
        
        **License**
        
        </h4>
        
        <h6 align="center">
        
        Apache License c [Jiaxiang Li;Shuyi Wang;Svitlana
        Galeshchuk](https://github.com/JiaxiangBU/dynamic_topic_modeling/blob/master/LICENSE.md)
        
        </h6>
        
        <div id="refs" class="references">
        
        <div id="ref-Blei2006Dynamic">
        
        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*.
        
        </div>
        
        <div id="ref-Svitlana_2019">
        
        Svitlana. 2019. "Dtmvisual: This Package Consists of Functionalities for
        Dynamic Topic Modelling and Its Visualization." GitHub. 2019.
        <https://github.com/GSukr/dtmvisual>.
        
        </div>
        
        <div id="ref-Shuyi_Wang2018">
        
        Wang, Shuyi. 2018. GitHub. 2018.
        <https://github.com/wshuyi/wei_lda_debate>.
        
        </div>
        
        </div>
        
Keywords: lda dynamic-topic-modeling
Platform: UNKNOWN
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Natural Language :: English
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Requires-Python: >=3.6
Description-Content-Type: text/markdown
