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Text Mining and Topic Modeling Toolkit

Project description

tmtookit: Text mining and topic modeling toolkit

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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. 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). It combines several known and well-tested packages such as SpaCy and SciPy.

At the moment, tmtoolkit focuses on methods around the Bag-of-words model, but word vectors (word embeddings) can also be generated.

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


Text preprocessing

tmtoolkit implements or provides convenient wrappers for several preprocessing methods, including:

All text preprocessing methods can operate in parallel to speed up computations with large datasets.

Topic modeling


  • 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)

Requirements and installation

For requirements and installation procedures, please have a look at the installation section in the documentation.


Code licensed under Apache License 2.0. See LICENSE file.

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