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TAToM: Text Analysis with Topic Models for the Humanities and Social Sciences

TAToM: Text Analysis with Topic Models for the Humanities and Social Sciences consists of a series of tutorials that introduce basic procedures in quantitative text analysis with a particular focus on the preparation of a text corpus for analysis and on exploratory analysis using topic models and machine learning.

Building

The project relies heavily on the ipython Sphinx directive, which is somewhat fragile.

Building the documentation may be accomplished by the following sequence of commands. The core requirements are: Python 3.3 or higher and Sphinx 1.2 or higher.

  1. Install required packages. The packages and version numbers are listed in requirements.txt. In theory the command below should download and install all the packages but in practice there are dependencies that prevent this from working. Some packages may need to be installed one by one.

    pip install -r requirements.txt

  2. The chapter illustrating the use of MALLET needs a java compiler, on Debian-based Linux such as Ubuntu you may install java with: ``sudo apt-get install

    sudo apt-get install openjdk-7-jre-headless

  3. Build the documentation:

    make html

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Quantitative Text Analysis for the digitale Geisteswissenschaften

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  • Python 73.5%
  • Makefile 15.4%
  • TeX 11.1%