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)

Recommending Music Titles

Consumer choices and how they can be influenced are critical factors for every business. For instance, most people are interested in specific music genres, have favorite authors, or engage in particular hobbies. This information can be extracted from their purchase history or product reviews, and when utilized correctly, it can drastically increase the company’s profit. A frequently cited case is the one million dollar prize awarded by Netflix in 2009 to a team that developed an algorithm that increased the accuracy of the company’s recommendation engine by 10%. In the end, as more user interactions occur on any online platform, more data is available for analysis, leading to superior customized recommendations.

This chapter seeks to exploit product and user data to create recommender systems for music titles. We will base the discussion on a corpus of customer reviews from the Amazon online store. First, we will perform exploratory data analysis...