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	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}
}

xelez/gensim