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Text Mining and Topic Modeling Toolkit

Project description

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tmtoolkit is a set of tools for text mining and topic modeling with Python developed especially for the use in the social sciences, linguistics, journalism or related disciplines. It aims for easy installation, extensive documentation and a clear programming interface while offering good performance on large datasets by the means of vectorized operations (via NumPy) and parallel computation (using Python’s multiprocessing module and the loky package). The basis of tmtoolkit’s text mining capabilities are built around SpaCy, which offers many language models.

The documentation for tmtoolkit is available on and the GitHub code repository is on

Requirements and installation

tmtoolkit works with Python 3.8 or newer (tested up to Python 3.11).

The tmtoolkit package is highly modular and tries to install as few dependencies as possible. For requirements and installation procedures, please have a look at the installation section in the documentation. For short, the recommended way of installing tmtoolkit is to create and activate a Python Virtual Environment (“venv”) and then install tmtoolkit with a recommended set of dependencies and a list of language models via the following:

pip install -U "tmtoolkit[recommended]"
# add or remove language codes in the list for installing the models that you need;
# don't use spaces in the list of languages
python -m tmtoolkit setup en,de

Again, you should have a look at the detailed installation instructions in order to install additional packages that enable more features such as topic modeling.


Text preprocessing

The tmtoolkit package offers several text preprocessing and text mining methods, including:

Wherever possible and useful, these methods can operate in parallel to speed up computations with large datasets.

Topic modeling

Other features


  • all languages are supported, for which SpaCy language models are available

  • all data must reside in memory, i.e. no streaming of large data from the hard disk (which for example Gensim supports)


If you’d like to contribute, please read the developer documentation first.


Code licensed under Apache License 2.0. See LICENSE file.

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