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)

Understanding sentiment analysis

You are running for public office, and to increase the chances of being elected, you must perform a substantial effort to persuade the voters. This undertaking becomes even more challenging for non-sympathizers and ambivalent citizens. Hence, a possible strategy is to focus on less favorable regions to your candidacy, which can be identified from the sentiment expressed in social media posts in this area. Similarly, suppose you are the CEO of a company that recently deployed a new product. This time, you are interested in knowing how your customers perceive it and in understanding their opinions. In both scenarios, you should also be concerned about the competition and the sentiment against your opponents’ political campaigns or competitor products. All these issues can be addressed by performing sentiment analysis: assigning a sentiment label to a piece of text. This task is the current chapter’s theme.

Recall the discussion in the...