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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 <https://loky.readthedocs.io/>_ package). The basis of tmtoolkit's text mining capabilities are built around
SpaCy <https://spacy.io/>, which offers many language models <https://spacy.io/models>.
The documentation for tmtoolkit is available on tmtoolkit.readthedocs.org <https://tmtoolkit.readthedocs.org>_ and
the GitHub code repository is on
github.com/internaut/tmtoolkit <https://github.com/internaut/tmtoolkit>_.
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 <https://tmtoolkit.readthedocs.io/en/latest/install.html>. For short,
the recommended way of installing tmtoolkit is to create and activate a
Python Virtual Environment ("venv") <https://docs.python.org/3/tutorial/venv.html> and then install tmtoolkit with
a recommended set of dependencies and a list of language models via the following:
.. code-block:: text
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 <https://tmtoolkit.readthedocs.io/en/latest/install.html>_ 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:
tokenization, sentence segmentation, part-of-speech (POS) tagging, named-entity recognition (NER) <https://tmtoolkit.readthedocs.io/en/latest/text_corpora.html#Configuring-the-NLP-pipeline,-parallel-processing-and-more-via-Corpus-parameters>_ (via SpaCy)lemmatization and token normalization <https://tmtoolkit.readthedocs.io/en/latest/preprocessing.html#Lemmatization-and-token-normalization>_pattern matching capabilities <https://tmtoolkit.readthedocs.io/en/latest/preprocessing.html#Common-parameters-for-pattern-matching-functions>_
(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) <https://tmtoolkit.readthedocs.io/en/latest/preprocessing.html#Keywords-in-context-(KWIC)-and-general-filtering-methods>_custom document and token attributes <https://tmtoolkit.readthedocs.io/en/latest/preprocessing.html#Working-with-document-and-token-attributes>_document and token attributes as dataframes <https://tmtoolkit.readthedocs.io/en/latest/preprocessing.html#Accessing-tokens-and-token-attributes>_visualizing corpus summary statistics <https://tmtoolkit.readthedocs.io/en/latest/preprocessing.html#Visualizing-corpus-summary-statistics>_collocations <https://tmtoolkit.readthedocs.io/en/latest/preprocessing.html#Identifying-and-joining-token-collocations>_token cooccurrences <https://tmtoolkit.readthedocs.io/en/latest/preprocessing.html#Token-cooccurrence-matrices>_splitting and sampling corpora <https://tmtoolkit.readthedocs.io/en/latest/text_corpora.html#Corpus-functions-for-document-management>_n-grams <https://tmtoolkit.readthedocs.io/en/latest/preprocessing.html#Generating-n-grams>_ and using
N-gram models <https://tmtoolkit.readthedocs.io/en/latest/api.html#module-tmtoolkit.ngrammodels>_sparse document-term matrices <https://tmtoolkit.readthedocs.io/en/latest/preprocessing.html#Generating-a-sparse-document-term-matrix-(DTM)>_Wherever possible and useful, these methods can operate in parallel to speed up computations with large datasets.
Topic modeling ^^^^^^^^^^^^^^
model computation in parallel <https://tmtoolkit.readthedocs.io/en/latest/topic_modeling.html#Computing-topic-models-in-parallel>_ for different copora
and/or parameter sets
support for lda <http://pythonhosted.org/lda/>,
scikit-learn <http://scikit-learn.org/stable/modules/generated/sklearn.decomposition.LatentDirichletAllocation.html>
and gensim <https://radimrehurek.com/gensim/>_ topic modeling backends
evaluation of topic models <https://tmtoolkit.readthedocs.io/en/latest/topic_modeling.html#Evaluation-of-topic-models>_ (e.g. in order to an optimal number
of topics for a given dataset) using several implemented metrics:
Mimno et al. 2011 <https://dl.acm.org/citation.cfm?id=2145462>) or with
metrics implemented in Gensim <https://radimrehurek.com/gensim/models/coherencemodel.html>)Arun et al. 2010 <http://doi.org/10.1007/978-3-642-13657-3_43>_)Wallach et al. 2009 <https://doi.org/10.1145/1553374.1553515>_)Cao Juan et al. 2009 <http://doi.org/10.1016/j.neucom.2008.06.011>_)Griffiths, Steyvers 2004 <http://doi.org/10.1073/pnas.0307752101>_)plotting of evaluation results <https://tmtoolkit.readthedocs.io/en/latest/topic_modeling.html#Evaluation-of-topic-models>_
common statistics for topic models <https://tmtoolkit.readthedocs.io/en/latest/topic_modeling.html#Common-statistics-and-tools-for-topic-models>_ such as
word saliency and distinctiveness (Chuang et al. 2012 <https://dl.acm.org/citation.cfm?id=2254572>), topic-word
relevance (Sievert and Shirley 2014 <https://www.aclweb.org/anthology/W14-3110>)
finding / filtering topics with pattern matching <https://tmtoolkit.readthedocs.io/en/latest/topic_modeling.html#Filtering-topics>_
export estimated document-topic and topic-word distributions to Excel <https://tmtoolkit.readthedocs.io/en/latest/topic_modeling.html#Displaying-and-exporting-topic-modeling-results>_
visualize topic-word distributions and document-topic distributions <https://tmtoolkit.readthedocs.io/en/latest/topic_modeling.html#Visualizing-topic-models>_
as word clouds or heatmaps
model coherence (Mimno et al. 2011 <https://dl.acm.org/citation.cfm?id=2145462>_) for individual topics
integrate PyLDAVis <https://pyldavis.readthedocs.io/en/latest/>_ to visualize results
Other features ^^^^^^^^^^^^^^
text files, tabular files (CSV or Excel), ZIP files or folders <https://tmtoolkit.readthedocs.io/en/latest/text_corpora.html#Loading-text-data>_splitting and joining documents <https://tmtoolkit.readthedocs.io/en/latest/text_corpora.html#Corpus-functions-for-document-management>_common statistics and transformations for document-term matrices <https://tmtoolkit.readthedocs.io/en/latest/bow.html>_ like word cooccurrence and tf-idfinteroperability with R <https://tmtoolkit.readthedocs.io/en/latest/rinterop.html>_SpaCy language models <https://spacy.io/models>_ are availableGensim <https://radimrehurek.com/gensim/>_ supports)If you'd like to contribute, please read the developer documentation <https://tmtoolkit.readthedocs.io/en/latest/development.html>_ first.
Code licensed under Apache License 2.0 <https://www.apache.org/licenses/LICENSE-2.0>.
See LICENSE <https://github.com/internaut/tmtoolkit/blob/master/LICENSE> file.
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FAQs
Text Mining and Topic Modeling Toolkit
We found that tmtoolkit demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 2 open source maintainers collaborating on the project.
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