Affective Computing: Historical Foundations, Current Applications, and Future Trends

Daily, Shaundra B.; James, Melva T.; Cherry, David; Porter, John J.; Darnell, Shelby S.; Isaac, Joseph; T, Roy

doi:10.1016/b978-0-12-801851-4.00009-4

Cited by 45 publications

(25 citation statements)

References 63 publications

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“…The role of emotions and sentiment have gained a lot of interest in HCI in recent years [5,10,45]. The research area concerned with the role of human emotions in HCI is nowadays often referred to as affective computing [45].…”

Section: Related Workmentioning

confidence: 99%

“…Two of the current major areas of this research branch are (1) the simulation of emotions in machines e. g. in robothuman interaction (e. g. Suzuki et al [43]) and the computational analysis, detection and reaction towards human emotions and sentiments in various systems (cf. Tao & Tan [45]; Daily et al [10]). With the advent of powerful machine learning technologies, this research area has shown significant progress in the last decade [10].…”

Section: Related Workmentioning

confidence: 99%

“…Tao & Tan [45]; Daily et al [10]). With the advent of powerful machine learning technologies, this research area has shown significant progress in the last decade [10]. The analysis and detection of human emotions rely on various forms of human cues, most importantly facial and speech-based cues [19].…”

Section: Related Workmentioning

confidence: 99%

“…However, advances in affective computing have led to the availability of easy-to-use general purpose emotion recognition tools for various modalities like text [23], speech and faces (cf. Daily et al [10]). There is little usage of these tools in the context of usability engineering so far.…”

Section: Introductionmentioning

confidence: 99%

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Investigating the Relationship Between Emotion Recognition Software and Usability Metrics

Schmidt

Schlindwein

Lichtner

et al. 2020

I-Com

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Due to progress in affective computing, various forms of general purpose sentiment/emotion recognition software have become available. However, the application of such tools in usability engineering (UE) for measuring the emotional state of participants is rarely employed. We investigate if the application of sentiment/emotion recognition software is beneficial for gathering objective and intuitive data that can predict usability similar to traditional usability metrics. We present the results of a UE project examining this question for the three modalities text, speech and face. We perform a large scale usability test (N = 125) with a counterbalanced within-subject design with two websites of varying usability. We have identified a weak but significant correlation between text-based sentiment analysis on the text acquired via thinking aloud and SUS scores as well as a weak positive correlation between the proportion of neutrality in users’ voice and SUS scores. However, for the majority of the output of emotion recognition software, we could not find any significant results. Emotion metrics could not be used to successfully differentiate between two websites of varying usability. Regression models, either unimodal or multimodal could not predict usability metrics. We discuss reasons for these results and how to continue research with more sophisticated methods.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Investigating the Relationship Between Emotion Recognition Software and Usability Metrics

Schmidt

Schlindwein

Lichtner

et al. 2020

I-Com

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show abstract

“…SA focuses on polarity detection in natural language processing (NLP), text analysis, or computational linguistics (Daily et al, 2017). The subtask of polarity detection is affective computing or emotion recognition that focuses on extracting a set of emotion labels from physiological signals, such as facial expression, posture, gesture, skin temperature, or hand tension (Cambria, 2016).…”

Section: Introductionmentioning

confidence: 99%

Using Machine Learning for Analyzing Sentiment Orientations Toward Eight Countries

Guo

Zhang

2020

SAGE Open

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By using machine learning technique, this article presents sentiment and concept analyses on 48,043 articles published in The Economist from 1991 through 2016. The Economist is one of the world’s most influential political and economic magazines. The article analyzes and compares the magazine’s sentiment orientations toward the Group of Seven’s ingroup member countries (Canada, France, Germany, Italy, Japan, the United Kingdom, and the United States) and its outgroup member country China. The sentiment analyses are performed on and compared between different periods of Clinton’s, Bush’s, and Obama’s administrations in the United States; Major’s, Blair’s, Brown’s, and Cameron’s cabinets in the United Kingdom; and Kohl’s, Schröder’s, and Merkel’s in Germany. The relationship between China hosting the Olympic Games or its growing economic power and the magazine’s sentiment orientations toward the country is examined. The concept analysis on the articles with extreme positivity or negativity shows that there is no difference between the ingroup and outgroup members in the topics covered in The Economist.

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Emotional Design

Zhou

Jiao

2021

Handbook of Human Factors and Ergonomics

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Affective Computing: Historical Foundations, Current Applications, and Future Trends

Cited by 45 publications

References 63 publications

Investigating the Relationship Between Emotion Recognition Software and Usability Metrics

Investigating the Relationship Between Emotion Recognition Software and Usability Metrics

Using Machine Learning for Analyzing Sentiment Orientations Toward Eight Countries

Emotional Design

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