Book Image

Machine Learning Techniques for Text

By : Nikos Tsourakis
Book Image

Machine Learning Techniques for Text

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 Spam Emails

Electronic mail is a ubiquitous internet service for exchanging messages between people. A typical problem in this sphere of communication is identifying and blocking unsolicited and unwanted messages. Spam detectors undertake part of this role; ideally, they should not let spam escape uncaught while not obstructing any non-spam.

This chapter deals with this problem from a machine learning (ML) perspective and unfolds as a series of steps for developing and evaluating a typical spam detector. First, we elaborate on the limitations of performing spam detection using traditional programming. Next, we introduce the basic techniques for text representation and preprocessing. Finally, we implement two classifiers using an open source dataset and evaluate their performance based on standard metrics.

By the end of the chapter, you will be able to understand the nuts and bolts behind the different techniques and implement them in Python. But, more importantly, you should be capable of seamlessly applying the same pipeline to similar problems.

We go through the following topics:

  • Obtaining the data
  • Understanding its content
  • Preparing the datasets for analysis
  • Training classification models
  • Realizing the tradeoffs of the algorithms
  • Assessing the performance of the models