Sentiment analysis from text consists of extracting information about opinions, sentiments,
and even emotions conveyed by writers towards topics of interest. It is often equated to …

Multi-label learning studies the problem where each example is represented by a single
instance while associated with a set of labels simultaneously. During the past decade …

This book is concerned with the classification of multilabeled data and other tasks related to
that subject. The goal of this chapter is to formally introduce the problem, as well as to give a …

Multi-label learning has received significant attention in the research community over the
past few years: this has resulted in the development of a variety of multi-label learning …

A large body of research in supervised learning deals with the analysis of single-label data,
where training examples are associated with a single label λ from a set of disjoint labels L …

A simple yet effective multilabel learning method, called label powerset (LP), considers each
distinct combination of labels that exist in the training set as a different class value of a single …

Multi-label Learning is a form of supervised learning where the classification algorithm is
required to learn from a set of instances, each instance can belong to multiple classes and …

In this paper, the automated detection of emotion in music is modeled as a multilabel
classification task, where a piece of music may belong to more than one class. Four …

The purpose of this paper is to analyze the imbalanced learning task in the multilabel
scenario, aiming to accomplish two different goals. The first one is to present specialized …

Learning from imbalanced data is a problem which arises in many real-world scenarios, so
does the need to build classifiers able to predict more than one class label simultaneously …