tmtoolkit 0.12.0

Text Mining and Topic Modeling Toolkit

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 tmtoolkit.readthedocs.org and the GitHub code repository is on github.com/internaut/tmtoolkit.

Requirements and installation

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

Note

There are two dependencies, that don’t work with Python 3.11 so far: lda and wordcloud. If you want to do topic modeling via LDA and/or want to use word cloud visualizations, you must use Python 3.8 to 3.10 or wait until lda and wordcloud receive updates that make them work under 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.

Features

Text preprocessing

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

• tokenization, sentence segmentation, part-of-speech (POS) tagging, named-entity recognition (NER) (via SpaCy)

• lemmatization and token normalization

• extensive pattern matching capabilities (exact matching, regular expressions or “glob” patterns) to be used in many methods of the package, e.g. for filtering on token or document level, or for keywords-in-context (KWIC)

• adding and managing custom document and token attributes

• accessing text corpora along with their document and token attributes as dataframes

• calculating and visualizing corpus summary statistics

• finding out and joining collocations

• calculating token cooccurrences

• splitting and sampling corpora

• generating n-grams and using N-gram models

• generating sparse document-term matrices

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

Topic modeling

• model computation in parallel for different copora and/or parameter sets

• support for lda, scikit-learn and gensim topic modeling backends

• evaluation of topic models (e.g. in order to an optimal number of topics for a given dataset) using several implemented metrics:

  • model coherence (Mimno et al. 2011) or with metrics implemented in Gensim)

  • KL divergence method (Arun et al. 2010)

  • probability of held-out documents (Wallach et al. 2009)

  • pair-wise cosine distance method (Cao Juan et al. 2009)

  • harmonic mean method (Griffiths, Steyvers 2004)

  • the loglikelihood or perplexity methods natively implemented in lda, sklearn or gensim

• plotting of evaluation results

• common statistics for topic models such as word saliency and distinctiveness (Chuang et al. 2012), topic-word relevance (Sievert and Shirley 2014)

• finding / filtering topics with pattern matching

• export estimated document-topic and topic-word distributions to Excel

• visualize topic-word distributions and document-topic distributions as word clouds or heatmaps

• model coherence (Mimno et al. 2011) for individual topics

• integrate PyLDAVis to visualize results

Other features

• loading and cleaning of raw text from text files, tabular files (CSV or Excel), ZIP files or folders

• splitting and joining documents

• common statistics and transformations for document-term matrices like word cooccurrence and tf-idf

• interoperability with R

Limits

• 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)

Contribute

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

License

Code licensed under Apache License 2.0. See LICENSE file.