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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

Visualization of the data

The vast majority of all human communication is visual. The reason is that we are wired to understand images instantly while we need to process text. For instance, visual artifacts such as maps have been around for centuries to help understand data, so it is not surprising that most people are visual learners and can easily retain the information they see. In addition, visuals make it much easier to spot patterns and identify anomalies, which is critical to people working with data. Technology ignited the need for better data visualizations to represent and present data.

A good visualization should encompass three characteristics: being trustworthy, accessible, and elegant. By saying it is trustworthy, we refer to the fact that the data is honestly portrayed. For example, if the visual suggests a relationship, trend, or correlation, the data should support that relationship; otherwise, we are just deceiving the audience. An accessible visualization refers...

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