From the Preface

What to Expect from This Book

In this book, we focus on applied machine learning for text analysis using the Python libraries just described. The applied nature of the book means that we focus not on the academic nature of linguistics or statistical models, but instead on how to be effective at deploying models trained on text inside of a software application.

The model for text analysis we propose is directly related to the machine learning workflow—a search process to find a model composed of features, an algorithm, and hyperparameters that best operates on training data to produce estimations on unknown data. This workflow starts with the construction and management of a training dataset, called a corpus in text analysis. We will then explore feature extraction and preprocessing methodologies to compose text as numeric data that machine learning can understand. With some basic features in hand, we explore techniques for classification and clustering on text, concluding the first few chapters of the book.

The latter chapters focus on extending models with richer features to create text-aware applications. We begin by exploring how context can be embedded as features, then move on to a visual interpretation of text to steering the model selection process. Next, we examine how to analyze complex relationships extracted from text using graph analysis techniques.

We then change focus to explore conversational agents and deepen our understanding of syntactic and semantic analysis of text. We conclude the book with a practical discussion of scaling text analysis with multiprocessing and Spark, and finally, we explore the next phase of text analytics: deep learning.

Who This Book Is For

This book is for Python programmers who are interested in applying natural language processing and machine learning to their software development toolkit. We don’t assume any special academic background or mathematical knowledge from our readers, and instead focus on tools and techniques rather than lengthy explanations. We do primarily analyze the English language in this book, so basic grammatical knowledge such as how nouns, verbs, adverbs, and adjectives are related to each other is helpful. Readers who are completely new to machine learning and linguistics but have a solid understanding of Python programming will not feel overwhelmed by the concepts we present.