Exploring Temporal Patterns in Classifying Frustrated and Delighted Smiles

Hoque, Mohammed Ehsan; McDuff, Daniel; Picard, Rosalind W.

doi:10.1109/t-affc.2012.11

Cited by 145 publications

(57 citation statements)

References 25 publications

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“…The emotional detection from facial expression is one of the most commonly and predominantly used methods [1,24,47]. In fact, facial expressions of basic emotions are widely believed to be naturally and universally expressed and recognized.…”

Section: Emotion Detectionmentioning

confidence: 99%

Emotions and personality traits in argumentation: An empirical evaluation1

Villata

Cabrio

Jraidi

et al. 2017

AAC

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Abstract. Argumentation is a mechanism to support different forms of reasoning such as decision making and persuasion and always cast under the light of critical thinking. In the latest years, several computational approaches to argumentation have been proposed to detect conflicting information, take the best decision with respect to the available knowledge, and update our own beliefs when new information arrives. The common point of all these approaches is that they assume a purely rational behavior of the involved actors, be them humans or artificial agents. However, this is not the case as humans are proved to behave differently, mixing rational and emotional attitudes to guide their actions. Some works have claimed that there exists a strong connection between the argumentation process and the emotions felt by people involved in such process. We advocate a complementary, descriptive and experimental method, based on the collection of emotional data about the way human reasoners handle emotions during debate interactions. Across different debates, people's argumentation in plain English is correlated with the emotions automatically detected from the participants, their engagement in the debate, and the mental workload required to debate. Results show several correlations among emotions, engagement and mental workload with respect to the argumentation elements. For instance, when two opposite opinions are conflicting, this is reflected in a negative way on the debaters' emotions. Beside their theoretical value for validating and inspiring computational argumentation theory, these results have applied value for developing artificial agents meant to argue with human users or to assist users in the management of debates.

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Section: Emotion Detectionmentioning

confidence: 99%

Emotions and personality traits in argumentation: An empirical evaluation1

Villata

Cabrio

Jraidi

et al. 2017

AAC

View full text Add to dashboard Cite

show abstract

“…McDuff et al presented results validating a novel framework for collecting and analyzing facial responses to media content over the Internet. The framework, data collected, and analysis demonstrated an ecologically valid method for unobtrusive evaluation of facial responses to media content [16,17].…”

Section: Related Workmentioning

confidence: 99%

“…2, the classification number r is respectively 1,2,6,7,14,16,19,22,25,31,35,37,38,40,43,44,45,46,47,48,50. r = 1 represents that all 50 users is in one class; r = 50 represents that each user is in one class respectively, namely 50 classes.…”

Section: Fuzzy Clusteringmentioning

confidence: 99%

A web sentiment analysis method on fuzzy clustering for mobile social media users

Yang

Geng

Liao

2016

J Wireless Com Network

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Micro-blog has become an emerging application in the Internet in recent years, and affective computing and sentiment analysis for micro-blog have been a vital research project in computer science, natural linguistics, psychology of human, and other social computing fields. In this paper, firstly, fuzzy clustering theory was introduced and source database for micro-blog was constructed. Then, word similarity computation method based on basic emotion word set of HowNet was used to calculate weights of micro-blog emotion words, and micro-blog emotional lexicon was built. Next, using calculation methods for appropriate sentiment value, the whole micro-blog message's emotional values were obtained. Finally, sentiment values from users in different time periods were selected as original data matrix, using the fuzzy clustering algorithm. The users were classified dynamically; meanwhile, dynamic clustering figure was generated. The best classification was obtained by using F statistics test method, and emotion trend graph was predicted from classification results, to more intuitively analyze emotion changes of user. In this paper, using micro-blog information with affective computing, governments, businesses, or enterprises can get different classification results according to the different needs and take the appropriate measures.

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“…They expressed 'smile' even though the situation where they are 'frustrated' or 'angry'. Facial expressions may not reflect the person's emotional state because of other factors such as a relationship with others and an atmosphere [7]. Therefore, we used 'smile' or 'the others' as a cue from facial expression module [8].…”

Section: International Journal Of Machine Learning Andmentioning

confidence: 99%

Automatic Engagement Level Estimation of Kids in a Learning Environment

Yun¹,

Park²,

Kim³

2015

IJMLC

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Abstract-This study is about the automatic engagement level measuring system which extracts features of kids taking tests with a desktop computer and estimates an engagement level. We recorded 12 kids from two difference kindergartens for 5 days. The test consists of 6 subjects with 2 sessions (levels). The recorded RGB video data is divided into 30 second video clips which are labeled by an expert. Cues reflecting face and head information are extracted from video data. The cues are aggregated for 30 seconds and used for estimating the engagement level. We used a relevance vector classifier to estimate an engagement level. We also analyze the data using linear regression analysis and find valid features. The system shows a promising performance of engagement level estimation of kids.Index Terms-Engagement level estimation, facial expression, facial feature, kid, multiple intelligence.

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Exploring Temporal Patterns in Classifying Frustrated and Delighted Smiles

Cited by 145 publications

References 25 publications

Emotions and personality traits in argumentation: An empirical evaluation1

Emotions and personality traits in argumentation: An empirical evaluation1

A web sentiment analysis method on fuzzy clustering for mobile social media users

Automatic Engagement Level Estimation of Kids in a Learning Environment

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