https://github.com/jiaxiangbu/dynamic_topic_modeling

dynamic topic modeling
https://github.com/jiaxiangbu/dynamic_topic_modeling

dynamic-topic-modeling lda

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dynamic topic modeling

• Host: GitHub
• URL: https://github.com/jiaxiangbu/dynamic_topic_modeling
• Owner: JiaxiangBU
• License: other
• Created: 2020-02-06T11:16:52.000Z (almost 6 years ago)
• Default Branch: master
• Last Pushed: 2023-02-05T07:51:42.000Z (almost 3 years ago)
• Last Synced: 2025-11-28T10:16:26.150Z (about 2 months ago)
• Topics: dynamic-topic-modeling, lda
• Language: Jupyter Notebook
• Homepage: https://jiaxiangbu.github.io/dynamic_topic_modeling/
• Size: 4.62 MB
• Stars: 42
• Watchers: 2
• Forks: 1
• Open Issues: 0
• Metadata Files:
  • Readme: README.Rmd
  • License: LICENSE.md
  • Citation: CITATION.bib

Awesome Lists containing this project

README

          
---

output: github_document

bibliography: [../learn_nlp/refs/add.bib,refs/add.bib]

---

```{r, include = FALSE}

knitr::opts_chunk$set(

  collapse = TRUE,

  comment = "#>",

  fig.path = "man/figures/README-",

  out.width = "100%"

)

```

# dynamic_topic_modeling

[![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)

Dynamic Topic Modeling (DTM)[@Blei2006Dynamic] is an advanced machine learning technique for uncovering the latent topics in a corpus of documents over time. The goal of this project is to provide an easy-to-use Python package for running DTM. This package is built on the frameworks of [sklearn](https://github.com/wshuyi/wei_lda_debate) and [gensim](https://github.com/GSukr/dtmvisual)[@Shuyi_Wang2018;@Svitlana_2019] for Dynamic Topic Modeling.

To get started, follow the tutorials on our [Jupyter notebooks](https://nbviewer.jupyter.org/github/JiaxiangBU/dynamic_topic_modeling/tree/master/):

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)

## Install

`pip install dynamic_topic_modeling`

## Citations

If you use dynamic_topic_modeling, please cite:

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}

}

```

`r add2pkg::add_disclaimer("Jiaxiang Li;Shuyi Wang;Svitlana Galeshchuk", license_name = "Apache License")`