dynamic-topic-modeling

dynamic topic modeling

https://github.com/jiaxiangbu/dynamic_topic_modeling

Science Score: 41.0%

This score indicates how likely this project is to be science-related based on various indicators:

  • CITATION.cff file
    Found CITATION.cff file
  • codemeta.json file
  • .zenodo.json file
  • DOI references
    Found 5 DOI reference(s) in README
  • Academic publication links
    Links to: zenodo.org
  • Committers with academic emails
  • Institutional organization owner
  • JOSS paper metadata
  • Scientific vocabulary similarity
    Low similarity (13.0%) to scientific vocabulary

Keywords

dynamic-topic-modeling lda
Last synced: 6 months ago · JSON representation ·

Repository

dynamic topic modeling

Basic Info
Statistics
  • Stars: 40
  • Watchers: 2
  • Forks: 1
  • Open Issues: 0
  • Releases: 0
Topics
dynamic-topic-modeling lda
Created about 6 years ago · Last pushed about 3 years ago
Metadata Files
Readme License Citation

README.Rmd

---
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")`

Owner

  • Name: Jiaxiang Li
  • Login: JiaxiangBU
  • Kind: user
  • Location: Shanghai, China

李家翔 | Reviewer of XGBoost | Commiter of workflowr,tidypredict | R package developer | R, Python, Hive user | Modeler to launch solutions into production

Citation (CITATION.bib)

@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}
}

GitHub Events

Total
  • Watch event: 2
Last Year
  • Watch event: 2

Committers

Last synced: almost 3 years ago

All Time
  • Total Commits: 43
  • Total Committers: 2
  • Avg Commits per committer: 21.5
  • Development Distribution Score (DDS): 0.023
Top Committers
Name Email Commits
Jiaxiang Li a****g@f****m 42
dependabot[bot] 4****]@u****m 1
Committer Domains (Top 20 + Academic)

Issues and Pull Requests

Last synced: 8 months ago

All Time
  • Total issues: 0
  • Total pull requests: 1
  • Average time to close issues: N/A
  • Average time to close pull requests: about 1 hour
  • Total issue authors: 0
  • Total pull request authors: 1
  • Average comments per issue: 0
  • Average comments per pull request: 0.0
  • Merged pull requests: 1
  • Bot issues: 0
  • Bot pull requests: 1
Past Year
  • Issues: 0
  • Pull requests: 0
  • Average time to close issues: N/A
  • Average time to close pull requests: N/A
  • Issue authors: 0
  • Pull request authors: 0
  • Average comments per issue: 0
  • Average comments per pull request: 0
  • Merged pull requests: 0
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Top Authors
Issue Authors
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  • dependabot[bot] (1)
Top Labels
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dependencies (1)

Packages

  • Total packages: 1
  • Total downloads:
    • pypi 31 last-month
  • Total dependent packages: 0
  • Total dependent repositories: 1
  • Total versions: 4
  • Total maintainers: 1
pypi.org: dynamic-topic-modeling

Run dynamic topic modeling

  • Versions: 4
  • Dependent Packages: 0
  • Dependent Repositories: 1
  • Downloads: 31 Last month
Rankings
Dependent packages count: 10.0%
Stargazers count: 10.8%
Average: 17.6%
Dependent repos count: 21.8%
Forks count: 22.7%
Downloads: 23.0%
Maintainers (1)
Last synced: 6 months ago

Dependencies

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