tmtoolkit: 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, 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 package). The basis of tmtoolkit’s text mining capabilities are built around SpaCy, which offers a many language models. Currently, the following languages are supported for text mining:
Catalan
Chinese
Danish
Dutch
English
French
German
Greek
Italian
Japanese
Lithuanian
Macedonian
Norwegian Bokmål
Polish
Portuguese
Romanian
Russian
Spanish
The documentation for tmtoolkit is available on tmtoolkit.readthedocs.org and the GitHub code repository is on github.com/WZBSocialScienceCenter/tmtoolkit.
Features
Text preprocessing and text mining
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)
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
generating n-grams
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
common statistics for topic models such as word saliency and distinctiveness (Chuang et al. 2012), topic-word relevance (Sievert and Shirley 2014)
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
common statistics and transformations for document-term matrices like word cooccurrence and tf-idf
Limits
only 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)
Built-in datasets
Currently tmtoolkit comes with the following built-in datasets which can be loaded via
tmtoolkit.corpus.Corpus.from_builtin_corpus
:
“en-NewsArticles”: News Articles (Dai, Tianru, 2017, “News Articles”, https://doi.org/10.7910/DVN/GMFCTR, Harvard Dataverse, V1)
random samples from ParlSpeech V2 (Rauh, Christian; Schwalbach, Jan, 2020, “The ParlSpeech V2 data set: Full-text corpora of 6.3 million parliamentary speeches in the key legislative chambers of nine representative democracies”, https://doi.org/10.7910/DVN/L4OAKN, Harvard Dataverse) for different languages:
“de-parlspeech-v2-sample-bundestag”
“en-parlspeech-v2-sample-houseofcommons”
“es-parlspeech-v2-sample-congreso”
“nl-parlspeech-v2-sample-tweedekamer”
About this documentation
This documentation guides you in several chapters from installing tmtoolkit to its specific use cases and shows some examples with built-in corpora and other datasets. All “hands on” chapters from Getting started to Topic modeling are generated from Jupyter Notebooks. If you want to follow along using these notebooks, you can download them from the GitHub repository.
There are also a few other examples as plain Python scripts available in the examples folder of the GitHub repository.
License
Code licensed under Apache License 2.0. See LICENSE file.
- Installation
- Getting started
- Working with text corpora
- Text preprocessing and basic text mining
- Optional: enabling logging output
- Loading example data
- Accessing tokens and token attributes
- Corpus vocabulary
- Visualizing corpus summary statistics
- Text processing: transforming documents and tokens
- Aside: A
Corpus
object as “state machine” - Lemmatization and token normalization
- Identifying and joining token collocations
- Visualizing corpus statistics of the transformed corpus
- Accessing the corpus documents as SpaCy documents
- Keywords-in-context (KWIC) and general filtering methods
- Working with document and token attributes
- Generating n-grams
- Generating a sparse document-term matrix (DTM)
- Serialization: Saving and loading
Corpus
objects
- Aside: A
- Working with the Bag-of-Words representation
- Topic modeling
- API
- tmtoolkit.bow
- tmtoolkit.corpus
- tmtoolkit.tokenseq
- tmtoolkit.topicmod
- Evaluation metrics for Topic Modeling
- Printing, importing and exporting topic model results
- Statistics for topic models and BoW matrices
- Parallel model fitting and evaluation with lda
- Parallel model fitting and evaluation with scikit-learn
- Parallel model fitting and evaluation with Gensim
- Visualize topic models and topic model evaluation results
- Base classes for parallel model fitting and evaluation
- tmtoolkit.utils
- Version history
- 0.11.0 - 2022-02-08
- 0.10.0 - 2020-08-03
- 0.9.0 - 2019-12-20
- 0.8.0 - 2019-02-05
- 0.7.3 - 2018-09-17 (last release to support Python 2.7)
- 0.7.2 - 2018-07-23
- 0.7.1 - 2018-06-18
- 0.7.0 - 2018-06-18
- 0.6.3 - 2018-06-01
- 0.6.2 - 2018-04-27
- 0.6.1 - 2018-04-27
- 0.6.0 - 2018-04-25
- 0.5.0 - 2018-02-13
- 0.4.2 - 2018-02-06
- 0.4.1 - 2018-01-24
- 0.4.0 - 2018-01-18