gensim – Topic Modelling in Python

Gensim is a Python library for topic modelling, document indexing and similarity retrieval with large corpora. Target audience is the natural language processing (NLP) and information retrieval (IR) community.

Features

• All algorithms are memory-independent w.r.t. the corpus size (can process input larger than RAM, streamed, out-of-core),
• Intuitive interfaces
  • easy to plug in your own input corpus/datastream (trivial streaming API)
  • easy to extend with other Vector Space algorithms (trivial transformation API)
• Efficient multicore implementations of popular algorithms, such as online Latent Semantic Analysis (LSA/LSI/SVD), Latent Dirichlet Allocation (LDA), Random Projections (RP), Hierarchical Dirichlet Process (HDP) or word2vec deep learning.
• Distributed computing: can run Latent Semantic Analysis and Latent Dirichlet Allocation on a cluster of computers.
• Extensive documentation and Jupyter Notebook tutorials.

If this feature list left you scratching your head, you can first read more about the Vector Space Model and unsupervised document analysis on Wikipedia.

Support

Please raise potential bugs on github. See Contribution Guide prior to raising an issue.

If you have an open-ended or a research question:

• Mailing List is the best option
• Gitter chat room is also available

Installation

This software depends on NumPy and Scipy, two Python packages for scientific computing. You must have them installed prior to installing gensim.

It is also recommended you install a fast BLAS library before installing NumPy. This is optional, but using an optimized BLAS such as ATLAS or OpenBLAS is known to improve performance by as much as an order of magnitude. On OS X, NumPy picks up the BLAS that comes with it automatically, so you don’t need to do anything special.

The simple way to install gensim is:

pip install -U gensim

Or, if you have instead downloaded and unzipped the source tar.gz package, you’d run:

python setup.py test
python setup.py install

For alternative modes of installation (without root privileges, development installation, optional install features), see the documentation.

This version has been tested under Python 2.7, 3.5 and 3.6. Gensim’s github repo is hooked against Travis CI for automated testing on every commit push and pull request. Support for Python 2.6, 3.3 and 3.4 was dropped in gensim 1.0.0. Install gensim 0.13.4 if you must use Python 2.6, 3.3 or 3.4. Support for Python 2.5 was dropped in gensim 0.10.0; install gensim 0.9.1 if you must use Python 2.5).

How come gensim is so fast and memory efficient? Isn’t it pure Python, and isn’t Python slow and greedy?

Many scientific algorithms can be expressed in terms of large matrix operations (see the BLAS note above). Gensim taps into these low-level BLAS libraries, by means of its dependency on NumPy. So while gensim-the-top-level-code is pure Python, it actually executes highly optimized Fortran/C under the hood, including multithreading (if your BLAS is so configured).

Memory-wise, gensim makes heavy use of Python’s built-in generators and iterators for streamed data processing. Memory efficiency was one of gensim’s design goals, and is a central feature of gensim, rather than something bolted on as an afterthought.

Documentation

Adopters

Name Logo URL Description

RaRe Technologies

rare-technologies.com Machine learning & NLP consulting and training. Creators and maintainers of Gensim.

Mindseye

mindseye.com Similarities in legal documents

Talentpair

talentpair.com Data science driving high-touch recruiting

Tailwind

Tailwindapp.com Post interesting and relevant content to Pinterest

Issuu

Issuu.com Gensim’s LDA module lies at the very core of the analysis we perform on each uploaded publication to figure out what it’s all about.

Sports Authority

sportsauthority.com Text mining of customer surveys and social media sources

Search Metrics

searchmetrics.com Gensim word2vec used for entity disambiguation in Search Engine Optimisation

Cisco Security

cisco.com Large-scale fraud detection

12K Research

12k.co Document similarity analysis on media articles

National Institutes of Health

github/NIHOPA Processing grants and publications with word2vec

Codeq LLC

codeq.com Document classification with word2vec

Mass Cognition

masscognition.com Topic analysis service for consumer text data and general text data

Stillwater Supercomputing

stillwater-sc.com Document comprehension and association with word2vec

Channel 4

channel4.com Recommendation engine

Amazon

amazon.com Document similarity

SiteGround Hosting

siteground.com An ensemble search engine which uses different embeddings models and similarities, including word2vec, WMD, and LDA.

Juju

www.juju.com Provide non-obvious related job suggestions.

NLPub

nlpub.org Distributional semantic models including word2vec.

Capital One

www.capitalone.com Topic modeling for customer complaints exploration.

Citing gensim

When citing gensim in academic papers and theses, please use this BibTeX entry:

@inproceedings{rehurek_lrec,
      title = {{Software Framework for Topic Modelling with Large Corpora}},
      author = {Radim {\v R}eh{\r u}{\v r}ek and Petr Sojka},
      booktitle = {{Proceedings of the LREC 2010 Workshop on New
           Challenges for NLP Frameworks}},
      pages = {45--50},
      year = 2010,
      month = May,
      day = 22,
      publisher = {ELRA},
      address = {Valletta, Malta},
      note={\url{http://is.muni.cz/publication/884893/en}},
      language={English}
}