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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

Extracting word embedding representation

We will start this section with an example to facilitate understanding. Suppose you are assigned to create the matching algorithm for a new dating service. This algorithm must identify people with similar characteristics and propose candidate profiles. Upon registering to the system, each user is asked a series of questions crafted to assess the five personality traits. The Big Five is a taxonomy for human personality and psyche. It includes extraversion, agreeableness, openness, conscientiousness, and neuroticism. Based on their answer, each user receives a score (percentage) for each trait according to the grayscale values of Figure 3.19:

Figure 3.19 – Grayscale values that signify the intensity of a characteristic

Figure 3.20 illustrates how we can visualize the users of the platform with a personalized grayscale vector that consists of five elements:

Figure 3.20 – Grayscale...

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