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You're reading from  F# for Machine Learning Essentials

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Published inFeb 2016
Reading LevelExpert
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ISBN-139781783989348
Edition1st Edition
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Sudipta Mukherjee
Sudipta Mukherjee
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Sudipta Mukherjee

Sudipta Mukherjee was born in Kolkata and migrated to Bangalore. He is an electronics engineer by education and a computer engineer/scientist by profession and passion. He graduated in 2004 with a degree in electronics and communication engineering. He has a keen interest in data structure, algorithms, text processing, natural language processing tools development, programming languages, and machine learning at large. His first book on Data Structure using C has been received quite well. Parts of the book can be read on Google Books. The book was also translated into simplified Chinese, available from Amazon.cn. This is Sudipta's second book with Packt Publishing. His first book, .NET 4.0 Generics , was also received very well. During the last few years, he has been hooked to the functional programming style. His book on functional programming, Thinking in LINQ, was released in 2014. He lives in Bangalore with his wife and son. Sudipta can be reached via e-mail at sudipto80@yahoo.com and via Twitter at @samthecoder.
Read more about Sudipta Mukherjee

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Handling negations


Sometimes, positive and negative polarities balance each other and a sentence for which you would expect to get a negative polarity ends up being an objective statement (meaning that the sentence doesn't have a polarity at all).

Consider the following sentence:

  • The camera of the phone was not good

The positive polarity of this sentence is calculated to be 0.625 (because of the word good) and the negative polarity of the sentence is calculated to be 0.625 (because of the word not). Thus, the overall polarity of this document is calculated to be zero; or in other words, the document is said to have no polarity at all. But as humans, we know that this phrase echoes a negative sentiment because the user is saying that the camera of the phone is not good.

In this section, we will see how we can tweak the above implementation to suit this type of sentence case.

The basic idea is to penalize a good word's positivity score with the value of the preceding negative words negative score...

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F# for Machine Learning Essentials
Published in: Feb 2016Publisher: ISBN-13: 9781783989348

Author (1)

author image
Sudipta Mukherjee

Sudipta Mukherjee was born in Kolkata and migrated to Bangalore. He is an electronics engineer by education and a computer engineer/scientist by profession and passion. He graduated in 2004 with a degree in electronics and communication engineering. He has a keen interest in data structure, algorithms, text processing, natural language processing tools development, programming languages, and machine learning at large. His first book on Data Structure using C has been received quite well. Parts of the book can be read on Google Books. The book was also translated into simplified Chinese, available from Amazon.cn. This is Sudipta's second book with Packt Publishing. His first book, .NET 4.0 Generics , was also received very well. During the last few years, he has been hooked to the functional programming style. His book on functional programming, Thinking in LINQ, was released in 2014. He lives in Bangalore with his wife and son. Sudipta can be reached via e-mail at sudipto80@yahoo.com and via Twitter at @samthecoder.
Read more about Sudipta Mukherjee