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You're reading from  Mastering Data Mining with Python - Find patterns hidden in your data

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Published inAug 2016
Reading LevelIntermediate
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ISBN-139781785889950
Edition1st Edition
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Megan Squire
Megan Squire
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Megan Squire

Megan Squire is a professor of computing sciences at Elon University. Her primary research interest is in collecting, cleaning, and analyzing data about how free and open source software is made. She is one of the leaders of the FLOSSmole.org, FLOSSdata.org, and FLOSSpapers.org projects.
Read more about Megan Squire

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What is topic modeling?


Just like with the keyword-based text summarization techniques we looked at in Chapter 7, Automatic Text Summarization, topic modeling also takes into account what words are used in a text. However, the focus of topic modeling is more about themes and concepts, and not solely about summarizing text. Topic models can be used for summarization, but they can also be used for many other goals:

  • Topic models can assist with organization of documents, for example, to group news articles together into a cohesive section

  • Topic models can help us make recommendations about what to read next by finding materials that have a topic list in common

  • Topic models can improve search results by revealing documents that may use a mix of different keywords but are about the same idea

One critical component of the type topic modeling we will investigate in this chapter is that the analyst does not need to know what the topics or keywords are in advance. Instead, the model is created in an...

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Mastering Data Mining with Python - Find patterns hidden in your data
Published in: Aug 2016Publisher: ISBN-13: 9781785889950

Author (1)

author image
Megan Squire

Megan Squire is a professor of computing sciences at Elon University. Her primary research interest is in collecting, cleaning, and analyzing data about how free and open source software is made. She is one of the leaders of the FLOSSmole.org, FLOSSdata.org, and FLOSSpapers.org projects.
Read more about Megan Squire