This open access book provides an introduction and an overview of learning to quantify (aka
“quantification”), ie the task of training estimators of class proportions in unlabeled data by …

Sentiment quantification is the task of training, by means of supervised learning, estimators
of the relative frequency (also called “prevalence”) of sentiment-related classes (such as …

This chapter is possibly the central chapter of the book, and looks at the various supervised
learning methods for learning to quantify that have been proposed over the years. These …

Quantification, also known as class prevalence estimation, is the supervised learning task in
which a model is trained to predict the prevalence of each class in a given bag of examples …

Quantification learning is a relatively new deep learning task. Differing from a classic
classification problem where the class of a single instance is predicted, a quantification …

In this chapter we discuss the experimental evaluation of quantification systems. We look at
evaluation measures for the various types of quantification systems (binary, single-label …

This chapter looks at other aspects of the “quantification landscape” that have not been
covered in the previous chapters, and discusses the evolution of quantification research …

This chapter provides the motivation for what is to come in the rest of the book by describing
the applications that quantification has been put at, ranging from improving classification …

This chapter sets the stage for the rest of the book by introducing notions fundamental to
quantification, such as class proportions, class distributions and their estimation, dataset …

In this chapter we look at a number of “advanced”(or niche) topics in quantification, including
quantification for ordinal data,“regression quantification”(the task that stands to regression …