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A review of affective computing

Soujanya Poria, +3 more
- 01 Sep 2017 - 
- Vol. 37, pp 98-125
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TLDR
This first of its kind, comprehensive literature review of the diverse field of affective computing focuses mainly on the use of audio, visual and text information for multimodal affect analysis, and outlines existing methods for fusing information from different modalities.
About: 
This article is published in Information Fusion.The article was published on 2017-09-01 and is currently open access. It has received 969 citations till now. The article focuses on the topics: Affective computing & Modality (human–computer interaction).

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References
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Proceedings ArticleDOI

Consumer-Oriented Tech Mining: Integrating the Consumer Perspective into Organizational Technology Intelligence - The Case of Autonomous Driving

Marc Egger, +1 more
TL;DR: This paper proposes a novel and comprehensive approach to collect user-generated content from the web and apply text mining to derive consumer perceptions and provides an initial indication of concurrent validity on the emerging technology of autonomous driving.
Book ChapterDOI

Semantic Text Classification of Emergent Disease Reports

Yi Zhang, +1 more
TL;DR: This paper shows that both keywords and sentence semantic features are useful and demonstrated that this integrated approach to semantic sentence classification of disease reporting is highly effective.
Book ChapterDOI

Multi-stream confidence analysis for audio-visual affect recognition

Zhihong Zeng, +3 more
TL;DR: A computing algorithm that uses audio and visual sensors to recognize a speaker’s affective state is explored and person-independent experimental results suggest that the use of stream exponents estimated on training data results in classification accuracy improvement of audio-visual affect recognition.
Proceedings ArticleDOI

Sentiment Value Propagation for an Integral Sentiment Dictionary Based on Commonsense Knowledge

Hui-Hsin Wu, +3 more
TL;DR: iSentiDictionary is derived, a Chinese sentiment dictionary with 28,248 concepts and corresponding sentiment values, derived from a self-training sentiment spreading activation to expand the sentiment values on Chinese Concept Net.

Speech Emotion Recognition Exploiting Acoustic and Linguistic Information Sources

G. Rigoll, +2 more
TL;DR: An overview of the existing pieces of the puzzle of Speech Emotion Recognition is given, novel impulses are introduced, and future directions in this young discipline are investigated.
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Frequently Asked Questions (9)
Q1. What contributions have the authors mentioned in the paper "A review of affective computing: from unimodal analysis to multimodal fusion" ?

This is the primary motivation behind their 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. In this paper, the authors 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. As part of this review, the authors 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. 

One important area of future research is to investigate novel approaches for advancing their understanding of the temporal dependency between utterances, i. e., the effect of utterance at time t on the utterance at time t+1. The progress in text classification research can play a major role in future of the multimodal affect analysis research. Future research should focus on answering this question. The use of deep learning for multimodal fusion can also be an important future work. 

The primary advantage of analyzing videos over textual analysis, for detecting emotions and sentiments from opinions, is the surplus of behavioral cues. 

For acoustic features, low-level acoustic features were extracted at frame level on each utterance and used to generate feature representation of the entire dataset, using the OpenSMILE toolkit. 

Whilst machine learning methods, for supervised training of the sentiment analysis system, are predominant in literature, a number of unsupervised methods such as linguistic patterns can also be found. 

Across the ages of people involved, and the nature of conversations, facial expressions are the primary channel for forming an impression of the subject’s present state of mind. 

The results on uncontrolled recordings (i.e., speech downloaded from a video-sharing website) revealed that the feature adaptation scheme significantly improved the unweighted and weighted accuracies of the emotion recognition system. 

In their literature survey, the authors have found more than 90% of studies reported visual modality as superior to audio and other modalities. 

To accommodate research in audio-visual fusion, the audio and video signals were synchronized with an accuracy of 25micro-seconds.