Metadata-Version: 2.1
Name: tmtoolkit
Version: 0.11.1
Summary: Text Mining and Topic Modeling Toolkit
Home-page: https://github.com/WZBSocialScienceCenter/tmtoolkit
Author: Markus Konrad
Author-email: markus.konrad@wzb.eu
License: Apache License 2.0
Project-URL: Bug Reports, https://github.com/WZBSocialScienceCenter/tmtoolkit/issues
Project-URL: Source, https://github.com/WZBSocialScienceCenter/tmtoolkit
Keywords: textmining textanalysis text mining analysis preprocessing topicmodeling topic modeling evaluation
Platform: UNKNOWN
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Science/Research
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Classifier: Topic :: Scientific/Engineering :: Information Analysis
Classifier: Topic :: Software Development :: Libraries :: Python Modules
Classifier: Topic :: Utilities
Requires-Python: >=3.8
Description-Content-Type: text/x-rst
Provides-Extra: textproc
Provides-Extra: textproc_extra
Provides-Extra: wordclouds
Provides-Extra: lda
Provides-Extra: sklearn
Provides-Extra: gensim
Provides-Extra: topic_modeling_eval_extra
Provides-Extra: test
Provides-Extra: doc
Provides-Extra: dev
Provides-Extra: recommended
Provides-Extra: all
License-File: LICENSE
License-File: AUTHORS.md

tmtoolkit: Text mining and topic modeling toolkit
=================================================

|pypi| |pypi_downloads| |rtd| |runtests| |coverage| |zenodo|

*tmtoolkit* is a set of tools for text mining and topic modeling with Python developed especially for the use in the
social sciences, in 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 a `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/WZBSocialScienceCenter/tmtoolkit <https://github.com/WZBSocialScienceCenter/tmtoolkit>`_.

**Upgrade note:**

Since Feb 8 2022, the newest version 0.11.0 of tmtoolkit is available on PyPI. This version features a new API
for text processing and mining which is incompatible with prior versions. It's advisable to first read the
first three chapters of the `tutorial <https://tmtoolkit.readthedocs.io/en/latest/getting_started.html>`_
to get used to the new API. You should also re-install tmtoolkit in a new virtual environment or completely
remove the old version prior to upgrading. See the
`installation instructions <https://tmtoolkit.readthedocs.io/en/latest/install.html>`_.

Requirements and installation
-----------------------------

**tmtoolkit works with Python 3.8 or newer (tested up to Python 3.10).**

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.

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) <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>`_
- extensive `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>`_
- adding and managing
  `custom document and token attributes <https://tmtoolkit.readthedocs.io/en/latest/preprocessing.html#Working-with-document-and-token-attributes>`_
- accessing text corpora along with their
  `document and token attributes as dataframes <https://tmtoolkit.readthedocs.io/en/latest/preprocessing.html#Accessing-tokens-and-token-attributes>`_
- calculating and `visualizing corpus summary statistics <https://tmtoolkit.readthedocs.io/en/latest/preprocessing.html#Visualizing-corpus-summary-statistics>`_
- finding out and joining `collocations <https://tmtoolkit.readthedocs.io/en/latest/preprocessing.html#Identifying-and-joining-token-collocations>`_
- `splitting and sampling corpora <https://tmtoolkit.readthedocs.io/en/latest/text_corpora.html#Corpus-functions-for-document-management>`_
- generating `n-grams <https://tmtoolkit.readthedocs.io/en/latest/preprocessing.html#Generating-n-grams>`_
- generating `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:

   * model coherence (`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>`_)
   * KL divergence method (`Arun et al. 2010 <http://doi.org/10.1007/978-3-642-13657-3_43>`_)
   * probability of held-out documents (`Wallach et al. 2009 <https://doi.org/10.1145/1553374.1553515>`_)
   * pair-wise cosine distance method (`Cao Juan et al. 2009 <http://doi.org/10.1016/j.neucom.2008.06.011>`_)
   * harmonic mean method (`Griffiths, Steyvers 2004 <http://doi.org/10.1073/pnas.0307752101>`_)
   * the loglikelihood or perplexity methods natively implemented in lda, sklearn or gensim

* `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
^^^^^^^^^^^^^^

- loading and cleaning of raw text from
  `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-idf*

Limits
------

* all languages are supported, for which `SpaCy language models <https://spacy.io/models>`_ are available
* all data must reside in memory, i.e. no streaming of large data from the hard disk (which for example
  `Gensim <https://radimrehurek.com/gensim/>`_ supports)

License
-------

Code licensed under `Apache License 2.0 <https://www.apache.org/licenses/LICENSE-2.0>`_.
See `LICENSE <https://github.com/WZBSocialScienceCenter/tmtoolkit/blob/master/LICENSE>`_ file.

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