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    <p><strong>Existing Implementations of Naive Bayes</strong></p> <p>You would probably be better off just using one of the existing packages that supports document classification using naive Bayes, e.g.:</p> <p><strong>Python</strong> - To do this using the Python based <strong><a href="http://www.nltk.org/" rel="noreferrer">Natural Language Toolkit (NLTK)</a></strong>, see the <a href="http://nltk.googlecode.com/svn/trunk/doc/book/ch06.html#document-classification" rel="noreferrer"><strong>Document Classification</strong></a> section in the freely available <a href="http://www.nltk.org/book" rel="noreferrer">NLTK book</a>.</p> <p><strong>Ruby</strong> - If Ruby is more of your thing, you can use the <strong><a href="http://classifier.rubyforge.org/" rel="noreferrer">Classifier</a></strong> gem. Here's sample code that detects <a href="http://www.igvita.com/2007/05/23/bayes-classification-in-ruby/" rel="noreferrer"> whether Family Guy quotes are funny or not-funny</a>.</p> <p><strong>Perl</strong> - Perl has the <a href="http://search.cpan.org/dist/Algorithm-NaiveBayes/lib/Algorithm/NaiveBayes.pm" rel="noreferrer"><strong>Algorithm::NaiveBayes</strong></a> module, complete with a sample usage snippet in the package <a href="http://search.cpan.org/dist/Algorithm-NaiveBayes/lib/Algorithm/NaiveBayes.pm#SYNOPSIS" rel="noreferrer">synopsis</a>. </p> <p><strong>C#</strong> - C# programmers can use <strong><a href="http://nbayes.codeplex.com/" rel="noreferrer">nBayes</a></strong>. The project's home page has sample code for a simple spam/not-spam classifier.</p> <p><strong>Java</strong> - Java folks have <strong><a href="http://classifier4j.sourceforge.net/" rel="noreferrer">Classifier4J</a></strong>. You can see a training and scoring code snippet <a href="http://classifier4j.sourceforge.net/usage.html#Using_BayesianClassifier" rel="noreferrer">here</a>. </p> <p><strong>Bootstrapping Classification from Keywords</strong></p> <p>It sounds like you want to start with a set of keywords that are <strong>known to cue for certain topics</strong> and then use those keywords to <a href="http://en.wikipedia.org/wiki/Bootstrapping_%28machine_learning%29" rel="noreferrer"><strong>bootstrap a classifier</strong></a>. </p> <p>This is a reasonably clever idea. Take a look at the paper <a href="http://www.kamalnigam.com/papers/keywordcat-aclws99.pdf" rel="noreferrer"><strong>Text Classication by Bootstrapping with Keywords, EM and Shrinkage</strong></a> by McCallum and Nigam (1999). By following this approach, they were able to improve classification accuracy from the 45% they got by using hard-coded keywords alone to 66% using a bootstrapped Naive Bayes classifier. For their data, the latter is close to human levels of agreement, as people agreed with each other about document labels 72% of the time.</p>
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