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Probabilistic Latent Semantic Analysis

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A python implementation of Probabilistic Latent Semantic Analysis

What PLSA can do for you

Broadly speaking, PLSA is a tool of Natural Language Processing (NLP). It analyses a collection of text documents (a corpus) under the assumption that there are (by far) fewer topics to write about than there are documents in the corpus. It then tries to identify these topics (in terms of words and their relative importance to each topic) and to give you the relative importance of a pre-specified number of topics in each document.

In doing so, it does not actually try to "make sense" of each document (or "understand" it) by contextually analysing it. Rather, it simply counts how often which word occurs in each document, regardless of the context in which they occur. As such, it belongs to the family of so-called bag-of-words models.

In reducing a large number of documents to a much smaller number of topics, PSLA can be seen as an example of unsupervised dimensionality reduction, most related to non-negative matrix factorization.

To give an example, a bunch of documents might frequently contain words like "eating", "nutrition", "health", etc. Others might contain words like "state", "party", "ministry", etc. Yet others might contain words like "tournament", "ranking", "win", etc. It is easy to imagine there being documents that contain a mixture of these words. Not knowing in advance how many topics there are, one would have to run PLSA with several different numbers of topics and see the results to judge how many is a good choice. Picking three in our example would yield topics that could be described as "food", "politics", and "sports" and, while a number of documents will emerge as being purely about one of these topics, it is easy to imagine that there are others that have contributions from more than one topic (e.g., about a new initiative from the ministry of health, combining "food" and "politics"). PLSA will give you that mixture.


This code is available on the python package index PyPi. To install, I strongly recommend setting up a new virtual python environment, and then type

pip install plsa

on the console.

WARNING: On first use, some components of nltk that don't come with it out-of-the-box wil be downloaded. Should you install (against my express recommendation) install the plsa package system-wide (with sudo), then you lack the access rights to write the required nltk data to where it is supposed to go (into a subfolder of the plsa package directory).


This package depends on the following python packages:

  • numpy
  • matplotlib
  • wordcould
  • nltk

If you want to run the example notebook, you will also need to install the jupyter package.

Getting Started

Clone the GitHub repository and run the jupyter notebook Examples.ipynb in the notebooks folder.


Read the API documentation on Read the Docs

Technical considerations

The matrices to store and manipulate data can easily get quite large. That means you will soon run out of memory when toying with a large corpus. This could be mitigated to some extent by using sparse matrices. But since there is no built-in support for sparse matrices of more than 2 dimensions (we need 3) in scipy, this is not implemented.

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