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Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"---\noutput: github_document\nbibliography: [../learn_nlp/refs/add.bib,refs/add.bib]\n---\n\n\u003c!-- README.md is generated from README.Rmd. Please edit that file --\u003e\n\n```{r, include = FALSE}\nknitr::opts_chunk$set(\n  collapse = TRUE,\n  comment = \"#\u003e\",\n  fig.path = \"man/figures/README-\",\n  out.width = \"100%\"\n)\n```\n\n# dynamic_topic_modeling\n\n\u003c!-- badges: start --\u003e\n[![PyPI version](https://badge.fury.io/py/dynamic-topic-modeling.svg)](https://badge.fury.io/py/dynamic-topic-modeling)\n[![DOI](https://zenodo.org/badge/238671296.svg)](https://zenodo.org/badge/latestdoi/238671296)\n\u003c!-- badges: end --\u003e\n\nDynamic 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.\n\nTo get started, follow the tutorials on our [Jupyter notebooks](https://nbviewer.jupyter.org/github/JiaxiangBU/dynamic_topic_modeling/tree/master/):\n\n1. [LDA based on sklearn](https://nbviewer.jupyter.org/urls/jiaxiangbu.github.io/dynamic_topic_modeling/sklearn-lda.ipynb)\n2. [LDA based on gensim](https://nbviewer.jupyter.org/urls/jiaxiangbu.github.io/dynamic_topic_modeling/gensim-lda.ipynb)\n3. [Dynamic Topic Modeling](https://nbviewer.jupyter.org/urls/jiaxiangbu.github.io/dynamic_topic_modeling/dtm.ipynb)\n4. [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)\n\n## Install\n\n`pip install dynamic_topic_modeling`\n\n## Citations\n\n\nIf you use dynamic_topic_modeling, please cite:\n\nJiaxiang 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\n\n```\n@software{jiaxiang_li_2020_3660401,\n  author       = {Jiaxiang Li},\n  title        = {{JiaxiangBU/dynamic_topic_modeling: \n                   dynamic_topic_modeling 1.1.0}},\n  month        = feb,\n  year         = 2020,\n  publisher    = {Zenodo},\n  version      = {v1.1.0},\n  doi          = {10.5281/zenodo.3660401},\n  url          = {https://doi.org/10.5281/zenodo.3660401}\n}\n```\n\n`r add2pkg::add_disclaimer(\"Jiaxiang Li;Shuyi Wang;Svitlana Galeshchuk\", license_name = \"Apache License\")`\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fjiaxiangbu%2Fdynamic_topic_modeling","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fjiaxiangbu%2Fdynamic_topic_modeling","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fjiaxiangbu%2Fdynamic_topic_modeling/lists"}