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, 0.1.0.2, 0.1.0.3, 0.1.0.4
Change log	None available
Dependencies	base (>=4.7 && <5), containers, hext, text [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 2016-07-27T20:12:06Z

Modules

[Index]

NLP
- Hext
  - NLP.Hext.NaiveBayes

Downloads

hext-0.1.0.2.tar.gz [browse] (Cabal source package)
Package description (as included in the package)

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aneksteind

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Readme for hext-0.1.0.2

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hext

This is currently the beginning of a text classification library.

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 run through the makeMaterial function in NLP.Hext.NaiveBayes.

Before doing this, however, you must create a data type, its data constructors each representing a class to label each text sample with. An example of this can be seen in app/Main.hs where data Class = Positive | Negative deriving (Eq, Show) to label movie reviews as positive or negative.

Now that the learning material has been made with makeMaterial, it, along with a new string for the algorithm to classify, can be passed into runBayes like so: runBayes material "This is a sample review". An example, along with sample data, can be seen in app/Main.hs

hackage - https://hackage.haskell.org/package/hext-0.1.0.2