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Machine Learning Techniques for Text

You're reading from   Machine Learning Techniques for Text Apply modern techniques with Python for text processing, dimensionality reduction, classification, and evaluation

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Product type Paperback
Published in Oct 2022
Publisher Packt
ISBN-13 9781803242385
Length 448 pages
Edition 1st Edition
Languages
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Author (1):
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Nikos Tsourakis Nikos Tsourakis
Author Profile Icon Nikos Tsourakis
Nikos Tsourakis
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Table of Contents (13) Chapters Close

Preface 1. Chapter 1: Introducing Machine Learning for Text 2. Chapter 2: Detecting Spam Emails FREE CHAPTER 3. Chapter 3: Classifying Topics of Newsgroup Posts 4. Chapter 4: Extracting Sentiments from Product Reviews 5. Chapter 5: Recommending Music Titles 6. Chapter 6: Teaching Machines to Translate 7. Chapter 7: Summarizing Wikipedia Articles 8. Chapter 8: Detecting Hateful and Offensive Language 9. Chapter 9: Generating Text in Chatbots 10. Chapter 10: Clustering Speech-to-Text Transcriptions 11. Index 12. Other Books You May Enjoy

Detecting Hateful and Offensive Language

Sparked by the alarming situation on social media platforms, where there is a dramatic increase in inflammatory language, companies have already implemented algorithms to regulate or even remove extreme posts. On the other hand, freedom of opinion and expression is a cornerstone of many societies, raising concerns that attempts to curb inappropriate language could also lead to the restraint of free speech. The current chapter aims to identify hateful and offensive language in tweets. Without delving into the particulars of this debate, we will address a few technical challenges and provide possible solutions in this setting. During this process, we also introduce many new concepts and techniques for machine learning.

A central theme of this chapter concerns the reuse and tuning of third-party models to minimize the effort of a new deployment. Using an open source dataset with hateful and offensive tweets, we will examine the steps to build...

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