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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

Introducing logistic regression

Linear regression is well suited when predicting the value of a continuous numerical variable. Based on the assumption that there is a linear relationship between the dependent and the independent variable, the method aims to find the line of best fit and use it for prediction. In this chapter, however, we are dealing with a classification problem, as we need to assign a sentiment label (positive or negative) to a piece of text. Consequently, this is a different problem because the dependent variable is categorical and not numerical.

This section applies a supervised learning algorithm called logistic regression, which is suitable for binary classification problems. Notice that there is also the multinomial logistic regression algorithm option for multiclass problems. Logistic regression is a parametric learning algorithm that outputs a probability that an input belongs to a particular class. Instead of fitting a straight line to the data, the effort...

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