Computational Methods for Affect Detection from Natural Language
A broad overview of natural language processing in affective computing is given by this title. Its goal is to familiarize the reader with current approaches in affective computing as well as the most relevant concepts related to this field (affect, sentiment, subjectivity and others).
Research in human affect has a long established tradition in social sciences - Philosophy, Psychology, Socio-psychology, Cognitive Science, Pragmatics, Marketing, Communication. The study of affect from a computational point of view is a recent field in Artificial Intelligence, denominated “Affective Computing”. Despite the novelty of the subject, the volume and importance of research in automatic human affect recognition, classification and simulation has been constantly growing in the past decades, leading to the development of further sub-areas of research. One of these directions deals with the study of automatic affect treatment from text, in the Artificial Intelligence area of Natural Language Processing. In this context, different tasks have been developed, from emotion detection, subjectivity analysis, opinion mining to sentiment analysis and appraisal analysis.
Offers a broad overview of natural language processing in affective computing Familiarizes readers with current approaches in affective computing with explanations of theory as well as practical examples Introduces and defines the concepts employed in the computational treatment of affect from text, from an inter-disciplinary, unified perspective A timely topic for the age of social media