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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

Introducing social networks

In the late 1960s, the famous psychologist Stanley Milgram decided to investigate the small-world concept, which states that the entire world is connected through short chains of acquaintances. Performing an ingenious experiment, Milgram asked a few hundred people from various locations to get a letter to a stranger in Boston. The participants were given information about the target recipient and instructed to send the letter to someone they knew that would more likely know that individual. The following person in the chain had to repeat the same task and send the letter to someone even closer. When Milgram examined the letters that reached the target, he realized they had changed hands about six times on average. The result demonstrated that, on average, any two individuals in the US are separated by five connections, known by the phrase six degrees of separation.

Although this outcome can be debated for many reasons, it paved the way for similar experiments...

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