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.
- All algorithms are memory-independent w.r.t. the corpus size (can process input larger than RAM),
- 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 implementations of popular algorithms, such as online Latent Semantic Analysis (LSA/LSI), 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, and word2vec on multiple cores.
- Extensive HTML documentation and tutorials.
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 prior to 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.
The simple way to install gensim is:
sudo easy_install gensim
Or, if you have instead downloaded and unzipped the source tar.gz package, you'll need to run:
python setup.py test sudo 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 2.6 and 2.7. Python 3 support is work in progress.
Manual for the gensim package is available in HTML. It contains a walk-through of all its features and a complete reference section. It is also included in the source distribution package.
Gensim is open source software released under the GNU LGPL license. Copyright (c) 2009-2014 Radim Rehurek