A tutorial for topic-modeling with hierarchical stochastic blockmodels using graph-tool.
Based on the works in:
- Gerlach, M., Peixoto, T. P., & Altmann, E. G. (2018). A network approach to topic models. Science Advances, 4(7), eaaq1360. https://doi.org/10.1126/sciadv.aaq1360
- C. C. Hyland, Y. Tao, L. Azizi, M. Gerlach, T. P. Peixoto, and E. G. Altmann Multilayer Networks for Text Analysis with Multiple Data Types , EPJ Data Science 10, 33 (2021). https://epjdatascience.springeropen.com/articles/10.1140/epjds/s13688-021-00288-5
Get the code via: git clone https://github.com/martingerlach/hSBM_Topicmodel.git
We use the graph-tool package for finding topical structure in the word-document networks.
- see the installation-instructions, where you will find packages for linux, etc.
- for linux, one relatively straightforward way is to install via conda
conda create --name graph-tool python=3.7
conda activate graph-tool
conda install -c conda-forge gtk3 pygobject matplotlib graph-tool
The packages gtk3, pygobject, matplotlib are needed to enable plotting-functionality
We need some additional packages to run the code (for example, jupyter to run the tutorial-notebooks).
The list of packages is listed in requirements.txt
This method uses Stochastic block models for topic modeling of text.
Code-base: sbmtm.py
Tutorial-notebook: TopSBM-tutorial.ipynb guides you through the different steps to do topic modeling with stochastic block models
- How to construct the word-document network from a corpus of text
- How to fit the stochastic block model to the word-document network
- How to extract the topics from the fitted model, e.g.
- the most important words for each topic
- the clustering of documents
- the topic mixtures for each document
- How to visualize the topical structure, in particular the hierarchy of topics
The example-corpus is saved in corpus.txt
- each line is a separate document with words separated by whitespace
- optionally, we can provide a file with titles for the documents in
titles.txt
This method provides a multilayer extension to the Stochastic Block Model approach for topic modeling.
The code implementing a multilayer extension to the stochastic block model has been implemented for the 2-layer SBM containing a hyperlink and text layer. The addition of a metadata layer can be done by following the process for the addition of the hyperlink layer.
Code-base: sbmmultilayer.py
Tutorial-notebook: Multilayer_SBM_Tutorial.ipynb
The tutorial notebook details how to
- Construct a multilayer SBM with the hyperlink and text layer
- Fit a multilayer SBM using simulated annealing for improved inference
- Extract the consensus partitions from multiple runs of the fitting procedure
- Extract the topics and topic proportions associated to blocks of documents
The associated Wikipedia dataset is saved in a zip file in data/dataset-four.zip.
- Contains three 3 types of data.
- Hyperlink: Each Wikipedia article has a hyperlink to another Wikipedia article.
- Text: Each Wikipedia contains text associated to it.
- Metadata: Each Wikipedia has a category assigned to it by Wikipedia users.
The arxiv-dataset contains 2542 scientific papers from the High-Energy-Physics-Theory category on arxiv with 3 types of information:
- Directed links (paper A citing paper B)
- Text (title and abstract of the paper)
- Metadata (arxiv categories of the paper)
The dataset was dervied from the "High-energy physics theory citation network" dataset (available here) containing 27,770 papers by filtering those papers which contain at least one category besides the High-Energy-Physics-Theory category (all papers in this dataset contain the latter category).
Published Enron emails with ~0.5 million emails. The data can be obtained from here. It was obtained by the Federal Energy Regulatory Commission during its investigation of Enron's collapse.
The May 7, 2015 Version of dataset is uploaded here. A processed form of the Enron emails can be found here.