The text mining technique topic modeling has become a popular procedure for clustering documents into semantic groups. This application introduces a user-friendly workflow which leads from raw text data to an interactive visualization of the topic model. All you need is a text corpus and a little time.
“Topic modeling algorithms are statistical methods that analyze the words of the original texts to discover the themes that run through them, how those themes are connected to each other, and how they change over time.” – David M. Blei
- Getting started
- The application
- The sample corpus
- Example visualizations
- Troubleshooting
- Source code
- What is topic modeling?
- What is DARIAH-DE?
- License
Windows, macOS and Linux users do not have to install additional software. The application itself is portable.
- Go to the release-section and download the ZIP archive for your OS.
- Extract the content of the archive.
- Run the app by double-clicking the file
DARIAH Topics Explorer
.
You can also use the source code:
- Go to the release-section and download the source code as ZIP archive.
- Unzip the archive, e.g. using
unzip
via the command-line. - Make sure you have Pipenv installed (if not:
pip install --user pipenv
). - Run
pipenv install
to set up a virtual environment and install dependencies. - To start the application, type
pipenv run python topicsexplorer.py
, and press enter.
If you wish to use the sample corpus, you have to clone the repository with Git. See also section Sample corpus. If you download one of the archives (except the source code) from the release section, the corpus is included.
This application is designed to introduce topic modeling particularly gently (e.g. for educational purpose). If you have a very large text corpus, you may wish to use more powerful tools like MALLET, which is written in Java and can be completely controlled from the command-line. The topic modeling algorithm used in this application, latent Dirichlet allocation, was implemented by Allen B. Riddell using collapsed Gibbs sampling as described in Pritchard et al. (2000).
You might want to check out some Jupyter notebooks for topic modeling in Python – experimenting with an example corpus on Binder does not require any software on your local machine.
We provide a sample corpus (10 British novels) in this project. If you use Git, you can include the corpus, which is actually a submodule in this repository, by writing:
$ git clone --recursive https://github.com/DARIAH-DE/TopicsExplorer.git
or if you have already cloned the repository:
$ cd data/british-fiction-corpus
$ git submodule init
$ git submodule update
The following visualizations display the topic model output of 10 novels (written by Charles Dickens, George Eliot, Joseph Fielding, William Thackeray and Anthony Trollope).
Topics Explorer’s visualiztaions are interactive. You will be able to navigate through topics and documents, get similar topics and documents displayed, read excerpts from the original texts, and inspect the document-topic distributions in a heatmap.
Topics are probability distributions over the whole vocabulary of a text corpus. One value is assigned to each word, which indicates how relevant the word is to that topic (to be exact, how likely one word is to be found in a topic). After sorting those values in descending order, the first n words represent a topic.
Below the topics are ranked by their numerical dominance in the sample corpus; each bar displays a topic’s dominance score.
Each document consists to a certain extent of each topic, which is one of the theoretical assumptions of topic models. Although some values are too small to be visualized here (and have therefore been rounded to zero), they are actually greater than zero.
Visualizing the document-topic proportions in a heatmap displays the kind of information that is probably most useful. Going beyond pure exploration, it can be used to show thematic developments over a set of texts, akin to a dynamic topic model.
If you wish to use the application from source, you can either git clone
this repository, or download the ZIP archive.
Pipenv automatically creates and manages a virtualenv for this project. Install the tool as usual:
$ pip install pipenv
This application requires Python 3.6 – it is highly recommended to use pyenv for managing Python versions. Pipenv and pyenv works hand-in-hand.
To install the project’s dependencies:
$ pipenv install
After spawning a shell within the virtual environment, using pipenv shell
, you can run the application with:
$ python topicsexplorer.py
If you wish to access the application through your web browser, use the following command:
$ python topicsexplorer.py --browser
In general:
- Use the project’s issue tracker on GitHub. Feature requests are also explicitly welcome.
- Be patient. Depending on corpus size and number of iterations, the process may take some time, meaning something between some seconds and some hours.
Regarding the standalone executable:
- If the program displays an error message at startup, make sure that you have unpacked the archive.
- If you are on a Mac and get an error message saying that the file is from an “unidentified developer”, you can override it by holding control while double-clicking. The error message will still appear, but you will be given an option to run the file anyway.
- You might get a similar error message as the one above on Windows systems: “Windows Defender SmartScreen prevented an unrecognized app from starting”. If this is the case, please select “More Info” and then “Run anyway”.
- On a Windows machine, if you are not able to start the program, if nothing happens for a long time, or if you get an error message, go to the
src
folder, search for the filewebapp.exe
and click on it.
Regarding the source code:
- If you are unable to run Pipenv, e.g.
-bash: pipenv: command not found
, trypython -m pipenv
instead of onlypipenv
. Usepython3
instead ofpython
if you are on a Mac or on a Linux machine. - If you have problems with Pipenv, for example
ModuleNotFoundError: No module named 'pkg_resources.extern'
orCommand "python setup.py egg_info" failed with error code 1
, make sure that the current version ofsetuptools
is installed. You can fix that withpip install --upgrade setuptools
within the virtual environment. Usepip3
instead ofpip
if you are on a Mac or on a Linux machine. - If the application fails after pulling from GitHub, try updating the requirements in your virtual environment with
pipenv update
. - If you are on Linux and face issues with installing the dependencies (something with the library
regex
likePython.h not found
orx86_64-linux-gnu-gcc
not found), try installing the packagepython3-dev
withapt-get
first. - If you are on Ubuntu 18.04 and get the error
[1:1:0100/000000.576372:ERROR:broker_posix.cc(43)] Invalid node channel message
after running thetopicsexplorer.py
, runsudo apt-get install libglvnd-dev
in your command-line and try again.
- David M. Blei, Probabilisitic Topic Models, in : Communications of the ACM 55 (2012).
- Megan R. Brett, Topic Modeling, A Basic Introduction, in: Journal of Digital Humanities 2, 2012.
- David M. Blei, Topic Modeling and Digital Humanities, in: Journal of Digital Humanities 2 (2012).
- Matthew Jockers, David Mimno, Significant Themes in 19th-Century Literature, in: Poetics 41 (2013).
- Steffen Pielström, Severin Simmler, Thorsten Vitt, Fotis Jannidis, A Graphical User Interface for LDA Topic Modeling, in: Proceedings of the 28th Digital Humanities Conference (2018).
DARIAH-DE supports research in the humanities and cultural sciences with digital methods and procedures. The research infrastructure of DARIAH-DE consists of four pillars: teaching, research, research data and technical components. As a partner in DARIAH-EU, DARIAH-DE helps to bundle and network state-of-the-art activities of the digital humanities. Scientists use DARIAH, for example, to make research data available across Europe. The exchange of knowledge and expertise is thus promoted across disciplines and the possibility of discovering new scientific discourses is encouraged.
This application is developed with support from the DARIAH-DE initiative, the German branch of DARIAH-EU, the European Digital Research Infrastructure for the Arts and Humanities consortium. Funding has been provided by the German Federal Ministry for Research and Education (BMBF) under the identifier 01UG1610A to J.
This project is licensed under Apache 2.0. You can do what you like with the source code, as long as you include the original copyright, the full text of the Apache 2.0 license, and state significant changes. You cannot charge DARIAH-DE for damages, or use any of its trademarks like name or logos.