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		<title>Latent Dirichlet Allocation (LDA) and Topic ModelLing in Python</title>
		<link>https://petaminds.com/latent-dirichlet-allocation-lda-and-topic-modelling-in-python/</link>
					<comments>https://petaminds.com/latent-dirichlet-allocation-lda-and-topic-modelling-in-python/#respond</comments>
		
		<dc:creator><![CDATA[Tung Nguyen]]></dc:creator>
		<pubDate>Sun, 16 Jan 2022 22:09:59 +0000</pubDate>
				<category><![CDATA[data science]]></category>
		<category><![CDATA[latent dirichlet allocation]]></category>
		<category><![CDATA[lda]]></category>
		<category><![CDATA[modelling]]></category>
		<category><![CDATA[NLP]]></category>
		<category><![CDATA[topic]]></category>
		<guid isPermaLink="false">https://petaminds.com/?p=3590</guid>

					<description><![CDATA[<p>Topic modelling&#160;is a type of statistical modelling for discovering the abstract “topics” that occur in a collection of documents.&#160;Latent Dirichlet Allocation&#160;(LDA) is an example of a topic model and is used to classify text in a document to a particular topic.&#160;It builds a topic per document model and words per topic model, modelled as Dirichlet [&#8230;]</p>
<p>The post <a href="https://petaminds.com/latent-dirichlet-allocation-lda-and-topic-modelling-in-python/">Latent Dirichlet Allocation (LDA) and Topic ModelLing in Python</a> appeared first on <a href="https://petaminds.com">Petamind</a>.</p>
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		<title>Understanding Latent Dirichlet Allocation (LDA)</title>
		<link>https://petaminds.com/understanding-latent-dirichlet-allocation-lda/</link>
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		<dc:creator><![CDATA[Tung Nguyen]]></dc:creator>
		<pubDate>Sun, 02 Jan 2022 03:37:00 +0000</pubDate>
				<category><![CDATA[data science]]></category>
		<category><![CDATA[Research]]></category>
		<category><![CDATA[latent dirichlet allocation]]></category>
		<category><![CDATA[lda]]></category>
		<category><![CDATA[NLP]]></category>
		<guid isPermaLink="false">https://petaminds.com/?p=3625</guid>

					<description><![CDATA[<p>Imagine a large law firm takes over a smaller law firm and tries to identify the documents corresponding to different types of cases such as civil or criminal cases which the smaller firm has dealt or is currently dealing with. The presumption is that the documents are not already classified by the smaller law firm. [&#8230;]</p>
<p>The post <a href="https://petaminds.com/understanding-latent-dirichlet-allocation-lda/">Understanding Latent Dirichlet Allocation (LDA)</a> appeared first on <a href="https://petaminds.com">Petamind</a>.</p>
]]></description>
		
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