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)

Introducing content-based filtering

Systems based on content-based filtering exploit the properties of items to recommend new products with similar features. The statement that drives the central paradigm behind these recommenders is show me items similar to the ones I liked in the past. What can be considered properties of an item is an open issue and it is up to the system developer to define a proper set. Sometimes, it is evident from the samples; otherwise, we have to improvise and experiment to elicit the proper features. A poorly chosen set can negatively impact the outcome; this is where an experienced data scientist can make a difference.

This book’s focus on text data drives our decision on the properties to implement in the recommender system. Thus, we create a bag of words for each music item containing its review text and genres. We call it metadata:

# Group all tags per product id.
product_tags = pd.DataFrame(reviews.groupby('productId')[&apos...