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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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Toc

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

The data explosion

We live in a data-driven world that steadily becomes even more data-driven. The innate tendency of humans to impart information, especially in written form, has caused an abundance of data for various languages and domains. Besides people’s willingness to share information, advances in computer connectivity and storage have paved the way for an explosion in the volume of text data. For instance, hundreds of billions of emails are sent daily, and thousands of tweets are posted per second. Frantically, people and businesses are churning out lots of unstructured data with an increased volume, velocity, and variety, but with less veracity. The four Vs are defining properties of big data and shape our digital world. For that reason, they need some attention:

  • Volume: Big data is about this volume now reaching unprecedented heights. Digital storage has become so cheap and vast in its capacity that we can practically keep all the digital data we’re...
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