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Article in Journal ()

A review of affective computing: From unimodal analysis to multimodal fusion

Citation
Poria S, Cambria E, Bajpai R & Hussain A (2017) A review of affective computing: From unimodal analysis to multimodal fusion, Information Fusion, 37, pp. 98-125.

Abstract
Affective computing is an emerging interdisciplinary research field bringing together researchers and practitioners from various fields, ranging from artificial intelligence, natural language processing, to cognitive and social sciences. With the proliferation of videos posted online (e.g., on YouTube, Facebook, Twitter) for product reviews, movie reviews, political views, and more, affective computing research has increasingly evolved from conventional unimodal analysis to more complex forms of multimodal analysis. This is the primary motivation behind our first of its kind, comprehensive literature review of the diverse field of affective computing. Furthermore, existing literature surveys lack a detailed discussion of state of the art in multimodal affect analysis frameworks, which this review aims to address. Multimodality is defined by the presence of more than one modality or channel, e.g., visual, audio, text, gestures, and eye gage. In this paper, we focus mainly on the use of audio, visual and text information for multimodal affect analysis, since around 90% of the relevant literature appears to cover these three modalities. Following an overview of different techniques for unimodal affect analysis, we outline existing methods for fusing information from different modalities. As part of this review, we carry out an extensive study of different categories of state-of-the-art fusion techniques, followed by a critical analysis of potential performance improvements with multimodal analysis compared to unimodal analysis. A comprehensive overview of these two complementary fields aims to form the building blocks for readers, to better understand this challenging and exciting research field.

Keywords
Affective computing; Sentiment analysis; Multimodal affect analysis; Multimodal fusion; Audio, visual and text information fusion

StatusPublished
AuthorsPoria Soujanya, Cambria Erik, Bajpai Rajiv, Hussain Amir
Publication date09/2017
Publication date online03/02/2017
Date accepted by journal01/02/2017
PublisherElsevier
ISSN 1566-2535
LanguageEnglish

Journal
Information Fusion: Volume 37 (2017)

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