Book Image

Machine Learning Techniques for Text

By : Nikos Tsourakis
5 (1)
Book Image

Machine Learning Techniques for Text

5 (1)
By: Nikos Tsourakis

Overview of this book

With the ever-increasing demand for machine learning and programming professionals, it's prime time to invest in the field. This book will help you in this endeavor, focusing specifically on text data and human language by steering a middle path among the various textbooks that present complicated theoretical concepts or focus disproportionately on Python code. A good metaphor this work builds upon is the relationship between an experienced craftsperson and their trainee. Based on the current problem, the former picks a tool from the toolbox, explains its utility, and puts it into action. This approach will help you to identify at least one practical use for each method or technique presented. The content unfolds in ten chapters, each discussing one specific case study. For this reason, the book is solution-oriented. It's accompanied by Python code in the form of Jupyter notebooks to help you obtain hands-on experience. A recurring pattern in the chapters of this book is helping you get some intuition on the data and then implement and contrast various solutions. By the end of this book, you'll be able to understand and apply various techniques with Python for text preprocessing, text representation, dimensionality reduction, machine learning, language modeling, visualization, and evaluation.
Table of Contents (13 chapters)

Detecting Hateful and Offensive Language

Sparked by the alarming situation on social media platforms, where there is a dramatic increase in inflammatory language, companies have already implemented algorithms to regulate or even remove extreme posts. On the other hand, freedom of opinion and expression is a cornerstone of many societies, raising concerns that attempts to curb inappropriate language could also lead to the restraint of free speech. The current chapter aims to identify hateful and offensive language in tweets. Without delving into the particulars of this debate, we will address a few technical challenges and provide possible solutions in this setting. During this process, we also introduce many new concepts and techniques for machine learning.

A central theme of this chapter concerns the reuse and tuning of third-party models to minimize the effort of a new deployment. Using an open source dataset with hateful and offensive tweets, we will examine the steps to build...