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F# for Machine Learning Essentials

You're reading from  F# for Machine Learning Essentials

Product type Book
Published in Feb 2016
Publisher
ISBN-13 9781783989348
Pages 194 pages
Edition 1st Edition
Languages
Author (1):
Sudipta Mukherjee Sudipta Mukherjee
Profile icon Sudipta Mukherjee

Table of Contents (16) Chapters

F# for Machine Learning Essentials
Credits
Foreword
About the Author
Acknowledgments
About the Reviewers
www.PacktPub.com
Preface
1. Introduction to Machine Learning 2. Linear Regression 3. Classification Techniques 4. Information Retrieval 5. Collaborative Filtering 6. Sentiment Analysis 7. Anomaly Detection Index

Summary


Sentiment Analysis is a very active area of research right now. There are several aspects about this task that makes it very hard. In this chapter, the basic techniques have been discussed. However, sometimes the positive sentiment can be negatively perceived. For example, if someone is checking the public sentiment for a political figure then any statement with a positive sentiment towards an opponent of the political figure in question should actually be treated as a negative sentiment. Also, identifying sarcasm in phrases can be very challenging and that can lead to an increase in a false positive rate. Moreover, identifying the target of the sentiment automatically can be quite challenging. For example, consider the following statement the food was great but the décor was old-fashioned. If this statement appears for a website that rates restaurants, then identifying the sentiment towards food as positive but that for décor (another possible feature in the restaurant entity) as...

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