https://github.com/mimno/jsLDA

An implementation of latent Dirichlet allocation in javascript
https://github.com/mimno/jsLDA

Last synced: about 1 month ago
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An implementation of latent Dirichlet allocation in javascript

• Host: GitHub
• URL: https://github.com/mimno/jsLDA
• Owner: mimno
• License: mit
• Created: 2013-04-23T14:37:17.000Z (over 11 years ago)
• Default Branch: master
• Last Pushed: 2022-08-01T21:36:08.000Z (over 2 years ago)
• Last Synced: 2024-08-03T18:20:56.991Z (4 months ago)
• Language: JavaScript
• Size: 1.54 MB
• Stars: 181
• Watchers: 19
• Forks: 51
• Open Issues: 5
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

Awesome Lists containing this project

• awesome-topic-models - jsLDA - JavaScript implementation of LDA topic modeling in the browser (Models / Latent Dirichlet Allocation (LDA) [:page_facing_up:](https://www.jmlr.org/papers/volume3/blei03a/blei03a.pdf))

README

        
jsLDA

=====

An implementation of latent Dirichlet allocation in javascript. A [live demonstration](http://mimno.infosci.cornell.edu/jsLDA/jslda.html) is available.

Instructions:

------------

When you first load the page, it will request a file containing documents and a file containing stopwords. The default example is a corpus of paragraphs from US State of the Union speeches.

Click the "Run 50 iterations" button to start training. The browser may appear to freeze for a while.

Initially all words have been assigned randomly to topics.

We train a model by cycling through every word token in the documents and sampling a topic for that word.

An "iteration" corresponds to one pass through the documents.

The topics on the left side of the page should now look more interesting. Run more iterations if you would like -- there's probably still a lot of room for improvement after only 50 iterations.

Once you're satisfied with the model, you can click on a topic from the list on the right to sort documents in descending order by their use of that topic. Proportions are weighted so that longer documents will come first.

You can also explore correlations between topics by clicking the "Topic Correlations" tab. This view shows a force directed layout with connections between topics that have correlations above a certain threshold. You can control this threshold with the slider: a low cutoff will display more edges, while a high cutoff will remove all but the strongest correlations.

Topic correlations are actually pointwise mutual information scores. This score measures whether two topics occur

in the same document more often than we would expect by chance. Previous versions of this script calculated correlations

on logratios; PMI is simpler to calculate.

Using your own corpus:

---------------------

To use your own corpus, the best way is to place the files in this repository in the document root of a web server.

Replace the files `documents.txt` and `stoplist.txt` with your own corpus and stop list.

The documents file is a tab-delimited text file with one document per line. Each line has three fields:

    [doc ID] [tab] [label] [tab] [text...]

(this is the default format for Mallet). The "label" field is currently unused, but I plan to support timestamps, labels, etc.

The format for stopwords is one word per line. The "Vocabulary" tab may be useful in customizing a stoplist. Unicode is supported, so most languages that have meaningful whitespace (ie not CJK) should work.

The page works best in Chrome. Safari and Firefox work too, but may be considerably slower. It doesn't seem to work in IE.

Download results:

----------------

You can create reports about your topic model. Hit the `Downloads` tab.

Reports are in CSV format. The `sampling state` file contains the same information as a Mallet state file, but in a more compact format.