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	<title>Thinknook &#187; classification</title>
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	<link>http://thinknook.com</link>
	<description>Because the world needs another Business Intelligence blog!</description>
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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>
		<wfw:commentRss>http://thinknook.com/10-ways-to-improve-your-classification-algorithm-performance-2013-01-21/feed/</wfw:commentRss>
		<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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		<title>Generic Trend Classification Engine using Pearson Correlation Coefficient</title>
		<link>http://thinknook.com/approaching-trend-analysis-through-discretization-and-correlation-2012-12-16/?utm_source=rss&#038;utm_medium=rss&#038;utm_campaign=approaching-trend-analysis-through-discretization-and-correlation</link>
		<comments>http://thinknook.com/approaching-trend-analysis-through-discretization-and-correlation-2012-12-16/#comments</comments>
		<pubDate>Sun, 16 Dec 2012 22:45:38 +0000</pubDate>
		<dc:creator><![CDATA[Links Naji]]></dc:creator>
				<category><![CDATA[Data-Mining]]></category>
		<category><![CDATA[classification]]></category>
		<category><![CDATA[Pearson correlation]]></category>
		<category><![CDATA[social trends]]></category>
		<category><![CDATA[trend analysis]]></category>
		<category><![CDATA[trend classification]]></category>

		<guid isPermaLink="false">http://thinknook.com/?p=886</guid>
		<description><![CDATA[Trend analysis in my experience is generally done through manual (human) review and exploration of data through various BI tools, these tools do a great job by visually highlighting data that can be of interest to the data analyst, and when coupled with data-mining techniques such as clustering and forecasting, it gives us invaluable and [&#8230;]]]></description>
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		<slash:comments>0</slash:comments>
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		<item>
		<title>NLTK Megam (Maximum Entropy) Library on 64-bit Linux</title>
		<link>http://thinknook.com/nltk-megam-maximum-entropy-library-on-64-bit-linux-2012-11-27/?utm_source=rss&#038;utm_medium=rss&#038;utm_campaign=nltk-megam-maximum-entropy-library-on-64-bit-linux</link>
		<comments>http://thinknook.com/nltk-megam-maximum-entropy-library-on-64-bit-linux-2012-11-27/#comments</comments>
		<pubDate>Tue, 27 Nov 2012 15:57:46 +0000</pubDate>
		<dc:creator><![CDATA[Links Naji]]></dc:creator>
				<category><![CDATA[Coding]]></category>
		<category><![CDATA[Coding Libraries]]></category>
		<category><![CDATA[Data-Mining]]></category>
		<category><![CDATA[Sentiment Analysis]]></category>
		<category><![CDATA[classification]]></category>
		<category><![CDATA[logistic regression]]></category>
		<category><![CDATA[max ent]]></category>
		<category><![CDATA[megam]]></category>
		<category><![CDATA[natural language processing]]></category>
		<category><![CDATA[nltk]]></category>

		<guid isPermaLink="false">http://thinknook.com/?p=837</guid>
		<description><![CDATA[NLTK (Natural Language Toolkit) is a Python library that allows developers and researchers to extract information and annotations from text, and run classification algorithms such as the Naive Bayes or Maximum Entropy, as well as many other interesting Natural Language tools and processing techniques. The Maximum Entropy algorithm from NLTK comes in different flavours, this post will [&#8230;]]]></description>
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		<slash:comments>12</slash:comments>
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