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README.Rmd
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---
output: github_document
bibliography: [../learn_nlp/refs/add.bib,refs/add.bib]
---
<!-- README.md is generated from README.Rmd. Please edit that file -->
```{r, include = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
fig.path = "man/figures/README-",
out.width = "100%"
)
```
# dynamic_topic_modeling
<!-- badges: start -->
[](https://badge.fury.io/py/dynamic-topic-modeling)
[](https://zenodo.org/badge/latestdoi/238671296)
<!-- badges: end -->
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")`