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F# for Machine Learning Essentials

You're reading from   F# for Machine Learning Essentials Get up and running with machine learning with F# in a fun and functional way

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Product type Paperback
Published in Feb 2016
Publisher
ISBN-13 9781783989348
Length 194 pages
Edition 1st Edition
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Author (1):
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Sudipta Mukherjee Sudipta Mukherjee
Author Profile Icon Sudipta Mukherjee
Sudipta Mukherjee
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Table of Contents (9) Chapters Close

Preface 1. Introduction to Machine Learning FREE CHAPTER 2. Linear Regression 3. Classification Techniques 4. Information Retrieval 5. Collaborative Filtering 6. Sentiment Analysis 7. Anomaly Detection Index

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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