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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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Thumbs up? Sentiment Classiflcation using Machine Learning Techniques

Bo Pang, +2 more
TL;DR: In this paper, the problem of classifying documents not by topic, but by overall sentiment, e.g., determining whether a review is positive or negative, was considered and three machine learning methods (Naive Bayes, maximum entropy classiflcation, and support vector machines) were employed.
Proceedings Article

Recursive Deep Models for Semantic Compositionality Over a Sentiment Treebank

Richard Socher, +6 more
TL;DR: A Sentiment Treebank that includes fine grained sentiment labels for 215,154 phrases in the parse trees of 11,855 sentences and presents new challenges for sentiment compositionality, and introduces the Recursive Neural Tensor Network.
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Natural Language Processing (Almost) from Scratch

Ronan Collobert, +5 more
- 01 Feb 2011 - 
TL;DR: A unified neural network architecture and learning algorithm that can be applied to various natural language processing tasks including part-of-speech tagging, chunking, named entity recognition, and semantic role labeling is proposed.
Proceedings ArticleDOI

Thumbs up? Sentiment Classification using Machine Learning Techniques

Bo Pang, +2 more
TL;DR: This work considers the problem of classifying documents not by topic, but by overall sentiment, e.g., determining whether a review is positive or negative, and concludes by examining factors that make the sentiment classification problem more challenging.
Journal ArticleDOI

Active appearance models

Timothy F. Cootes, +2 more
- 01 Jun 2001 - 
Abstract: We describe a new method of matching statistical models of appearance to images. A set of model parameters control modes of shape and gray-level variation learned from a training set. We construct an efficient iterative matching algorithm by learning the relationship between perturbations in the model parameters and the induced image errors.
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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.