Text Mining and Topic Modeling Toolkit
tmtookit: 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. 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.
tmtoolkit implements or provides convenient wrappers for several preprocessing methods, including:
- tokenization and part-of-speech (POS) tagging (via SpaCy)
- lemmatization and term 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, document or document label level, or for keywords-in-context (KWIC)
- adding and managing custom token metadata
- accessing word vectors (word embeddings)
- generating n-grams
- generating sparse document-term matrices
- expanding compound words and “gluing” of specified subsequent tokens, e.g. ["White", "House"] becomes ["White_House"]
All text preprocessing methods can operate in parallel to speed up computations with large datasets.
model computation in parallel for different copora and/or parameter sets
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
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
Requirements and installation
For requirements and installation procedures, please have a look at the installation section in the documentation.
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