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	<title>Thinknook &#187; text classification</title>
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		<title>10 Tips to Improve your Text Classification Algorithm Accuracy and Performance</title>
		<link>http://thinknook.com/10-ways-to-improve-your-classification-algorithm-performance-2013-01-21/?utm_source=rss&#038;utm_medium=rss&#038;utm_campaign=10-ways-to-improve-your-classification-algorithm-performance</link>
		<comments>http://thinknook.com/10-ways-to-improve-your-classification-algorithm-performance-2013-01-21/#comments</comments>
		<pubDate>Mon, 21 Jan 2013 12:06:35 +0000</pubDate>
		<dc:creator><![CDATA[Links Naji]]></dc:creator>
				<category><![CDATA[Classification]]></category>
		<category><![CDATA[bigrams]]></category>
		<category><![CDATA[classification]]></category>
		<category><![CDATA[corpus]]></category>
		<category><![CDATA[predictiion]]></category>
		<category><![CDATA[stopwords]]></category>
		<category><![CDATA[text classification]]></category>
		<category><![CDATA[unigrams]]></category>

		<guid isPermaLink="false">http://thinknook.com/?p=934</guid>
		<description><![CDATA[In this article I discuss some methods you could adopt to improve the accuracy of your text classifier, I&#8217;ve taken a generalized approach so the recommendations here should really apply for most text classification problem you are dealing with, be it Sentiment Analysis, Topic Classification or any text based classifier. This is by no means [&#8230;]]]></description>
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		<slash:comments>17</slash:comments>
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		<title>Testing &amp; Diagnosing a Text Classification Algorithm</title>
		<link>http://thinknook.com/testing-diagnosing-a-text-classification-algorithm-2013-01-19/?utm_source=rss&#038;utm_medium=rss&#038;utm_campaign=testing-diagnosing-a-text-classification-algorithm</link>
		<comments>http://thinknook.com/testing-diagnosing-a-text-classification-algorithm-2013-01-19/#comments</comments>
		<pubDate>Sat, 19 Jan 2013 17:37:26 +0000</pubDate>
		<dc:creator><![CDATA[Links Naji]]></dc:creator>
				<category><![CDATA[Classification]]></category>
		<category><![CDATA[Data-Mining]]></category>
		<category><![CDATA[accuracy]]></category>
		<category><![CDATA[classification]]></category>
		<category><![CDATA[confusion matrix]]></category>
		<category><![CDATA[nltk]]></category>
		<category><![CDATA[precision]]></category>
		<category><![CDATA[recall]]></category>
		<category><![CDATA[text classification]]></category>

		<guid isPermaLink="false">http://thinknook.com/?p=922</guid>
		<description><![CDATA[To get something going with text (or any) classification algorithm is easy enough, all you need is an algorithm, such as Maximum Entropy or Naive Bayes, an implementation of each is available in many different flavors across various programming languages (I use NLTK on Python for text classification), and a bunch of already classified corpus data [&#8230;]]]></description>
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		<slash:comments>2</slash:comments>
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