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	<title>Thinknook &#187; Sentiment Analysis</title>
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		<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>

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		<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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		<title>Twitter Sentiment Analysis Training Corpus (Dataset)</title>
		<link>http://thinknook.com/twitter-sentiment-analysis-training-corpus-dataset-2012-09-22/?utm_source=rss&#038;utm_medium=rss&#038;utm_campaign=twitter-sentiment-analysis-training-corpus-dataset</link>
		<comments>http://thinknook.com/twitter-sentiment-analysis-training-corpus-dataset-2012-09-22/#comments</comments>
		<pubDate>Sat, 22 Sep 2012 16:13:49 +0000</pubDate>
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				<category><![CDATA[Sentiment Analysis]]></category>

		<guid isPermaLink="false">http://thinknook.com/?p=710</guid>
		<description><![CDATA[An essential part of creating a Sentiment Analysis algorithm (or any Data Mining algorithm for that matter) is to have a comprehensive dataset or corpus to learn from, as well as a test dataset to ensure that the accuracy of your algorithm meets the standards you expect. This will also allow you to tweak your [&#8230;]]]></description>
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