hext: a text classification library

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Warnings:

Exposed modules use unallocated top-level names: NLP

Please see README.md

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Properties

Versions	0.1.0.0, 0.1.0.1, 0.1.0.2, 0.1.0.3, 0.1.0.4, 0.1.0.4
Change log	None available
Dependencies	base (>=4.7 && <5), containers, hext, text, unordered-containers [details]
License	BSD-3-Clause
Copyright	2016 David Anekstein
Author	David Anekstein
Maintainer	aneksteind@gmail.com
Category	Natural Language Processing
Home page	https://github.com/aneksteind/hext#readme
Source repo	head: git clone https://github.com/aneksteind/hext
Uploaded	by aneksteind at 2016-09-17T15:02:44Z

Modules

[Index]

NLP
- Hext
  - NLP.Hext.NaiveBayes

Downloads

hext-0.1.0.4.tar.gz [browse] (Cabal source package)
Package description (as included in the package)

Maintainer's Corner

Package maintainers

aneksteind

For package maintainers and hackage trustees

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Readme for hext-0.1.0.4

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hext

This is currently the beginning of a text classification library.

##Installation/Running stack install hext

hackage - https://hackage.haskell.org/package/hext-0.1.0.3

To run:
stack build
stack exec hext-exe

##Usage

Currently, the only algorithm implementation is the Naive Bayes algorithm: to run your own data through this algorithm in order to classify your text, you need:

classified data: this can be sourced from a database where the only fields that are needed are the text itself, and it's class
a sample string which will be classified by the algorithm

In order to run the program, the classified data specified above must be converted into a BayesModel a using the teach function, where a is your own defined data type representing the class to classify your text. Your class must be and instance of Ord and Eq.

With your new BayesModel filled with knowledge, it's time to classify your text using runBayes. An example of this can be seen in app/Main.hs where data Class = Positive | Negative deriving (Eq, Ord, Show) to label movie reviews as either positive or negative.

##Contributing

I encourage contributing to this package, in the form of implementing algorithms that are not yet in the project, improving efficiency, or the like.