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Affective Computing

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Rosalind W. Picard – One of the best experts on this subject based on the ideXlab platform.

  • ACII (2) – Machine learning for Affective Computing
    Affective Computing and Intelligent Interaction, 2011
    Co-Authors: Mohammed Ehsan Hoque, Louis-philippe Morency, Daniel J Mcduff, Rosalind W. Picard

    Abstract:

    Affective Computing (AC) is a unique discipline which includes modeling affect using one or multiple modalities by drawing on techniques from many different fields. AC often deals with problems that are known to be very complex and multi-dimensional, involving different kinds of data (numeric, symbolic, visual etc.). However, with the advancement of machine learning techniques, a lot of those problems are now becoming more tractable.

    The purpose of this workshop was to engage the machine learning and Affective Computing communities towards solving problems related to understanding and modeling social Affective behaviors. We welcomed participation of researchers from diverse fields, including signal processing and pattern recognition, statistical machine learning, human-computer interaction, human-robot interaction, robotics, conversational agents, experimental psychology, and decision making.

    There is a need for a set of high standards for recognizing and understanding affect. At the same time, these standards need to take into account that the expectations and validations in this area may be different than in traditional research on machine learning. This should be reflected in the design of machine learning techniques used to tackle these problems. For example, Affective data sets are known to be noisy, high dimensional, and incomplete. Classes may overlap. Affective behaviors are often person specific and require temporal modeling with real-time performance. This first edition of the ACII Workshop on Machine Learning for Affective Computing will be a proper venue to invoke such discussions and engage the community towards design and validation of learning techniques for Affective Computing.

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  • Machine Learning for Affective Computing
    Media, 2011
    Co-Authors: Mohammed Hoque, Daniel J Mcduff, Louis-philippe Morency, Rosalind W. Picard

    Abstract:

    Affective Computing (AC) is a unique discipline which includes modeling affect using one or multiple modalities by drawing on techniques from many different fields. AC often deals with problems that are known to be very complex and multi-dimensional, involving different kinds of data (numeric, symbolic, visual etc.). However, with the advancement of machine learning techniques, a lot of those problems are now becoming more tractable. The purpose of this workshop was to engage the machine learning and Affective Computing communities towards solving problems related to understanding and modeling social Affective behaviors. We welcomed participation of researchers from diverse fields, including signal processing and pattern recognition, statistical machine learning, human-computer interaction, human-robot interaction, robotics, conversational agents, experimental psychology, and decision making. There is a need for a set of high standards for recognizing and understanding affect. At the same time, these standards need to take into account that the expectations and validations in this area may be different than in traditional research on machine learning. This should be reflected in the design of machine learning techniques used to tackle these problems. For example, Affective data sets are known to be noisy, high dimensional, and incomplete. Classes may overlap. Affective behaviors are often person specific and require temporal modeling with real-time performance. This first edition of the ACII Workshop on Machine Learning for Affective Computing will be a proper venue to invoke such discussions and engage the community towards design and validation of learning techniques for Affective Computing.

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  • Affective Computing: From laughter to IEEE
    IEEE Transactions on Affective Computing, 2010
    Co-Authors: Rosalind W. Picard

    Abstract:

    This is an invited introduction to the first issue of the IEEE Transactions on Affective Computing, telling personal stories and sharing viewpoints of a pioneer and visionary of the field of Affective Computing. This article is not intended to be a thorough or a historical account of the development of the field, for the author is not a historian and cannot begin to properly credit the extraordinary efforts of hundreds of people who helped to cultivate and expand the rich and fertile landscape that extends before us now.

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Mohammed Hoque – One of the best experts on this subject based on the ideXlab platform.

  • Machine Learning for Affective Computing
    Media, 2011
    Co-Authors: Mohammed Hoque, Daniel J Mcduff, Louis-philippe Morency, Rosalind W. Picard

    Abstract:

    Affective Computing (AC) is a unique discipline which includes modeling affect using one or multiple modalities by drawing on techniques from many different fields. AC often deals with problems that are known to be very complex and multi-dimensional, involving different kinds of data (numeric, symbolic, visual etc.). However, with the advancement of machine learning techniques, a lot of those problems are now becoming more tractable. The purpose of this workshop was to engage the machine learning and Affective Computing communities towards solving problems related to understanding and modeling social Affective behaviors. We welcomed participation of researchers from diverse fields, including signal processing and pattern recognition, statistical machine learning, human-computer interaction, human-robot interaction, robotics, conversational agents, experimental psychology, and decision making. There is a need for a set of high standards for recognizing and understanding affect. At the same time, these standards need to take into account that the expectations and validations in this area may be different than in traditional research on machine learning. This should be reflected in the design of machine learning techniques used to tackle these problems. For example, Affective data sets are known to be noisy, high dimensional, and incomplete. Classes may overlap. Affective behaviors are often person specific and require temporal modeling with real-time performance. This first edition of the ACII Workshop on Machine Learning for Affective Computing will be a proper venue to invoke such discussions and engage the community towards design and validation of learning techniques for Affective Computing.

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Mario Cannataro – One of the best experts on this subject based on the ideXlab platform.

  • BIBM – Sentiment analysis and Affective Computing for depression monitoring
    2017 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), 2017
    Co-Authors: Chiara Zucco, Barbara Calabrese, Mario Cannataro

    Abstract:

    Depression is one of the most common and disabling mental disorders that has a relevant impact on society. Semiautomatic and/or automatic health monitoring systems could be crucial and important to improve depression detection and follow-up. Sentiment Analysis refers to the use of natural language processing and text mining methodologies aiming to identify opinion or sentiment. Affective Computing is the study and development of systems and devices that can recognize, interpret, process, and simulate human affects. Sentiment Analysis and Affective Computing methodologies could provide effective tools and systems for an objective assessment and monitoring of psychological disorders and, in particular, of depression. In this paper, the application of sentiment analysis and Affective Computing methodologies to depression detection and monitoring are presented and discussed. Moreover, a preliminary design of an integrated multimodal system for depression monitoring, that includes sentiment analysis and Affective Computing techniques, is proposed. Specifically, the paper outlines the main issues and challenges relative to the design of such a system.

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  • BrainComp – Sentiment Analysis and Affective Computing: Methods and Applications
    Lecture Notes in Computer Science, 2016
    Co-Authors: Barbara Calabrese, Mario Cannataro

    Abstract:

    New Computing technologies, such as Affective Computing and sentiment analysis, are raising a strong interest in different fields, such as marketing, politics and, recently, life sciences. Examples of possible applications in the last field, regard the detection and monitoring of depressive states or mood disorders and anxiety conditions. This paper aims to provide an introductory overview of Affective Computing and sentiment analysis, through the discussion of the main processing techniques and applications. The paper concludes with a discussion relative to a new approach based on the integration of sentiment analysis and Affective Computing to obtain a more accurate and reliable detection of emotions and feelings for applications in the life sciences.

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