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Toward Culturally Relevant Emotion D...

Zanna, Khadija.

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Toward Culturally Relevant Emotion Detection Using Physiological Signals.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Toward Culturally Relevant Emotion Detection Using Physiological Signals./
Author:	Zanna, Khadija.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2020,
Description:	56 p.
Notes:	Source: Masters Abstracts International, Volume: 81-10.
Contained By:	Masters Abstracts International81-10.
Subject:	Computer science. -
Online resource:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=27744637
ISBN:	9798607321062

Toward Culturally Relevant Emotion Detection Using Physiological Signals.
Zanna, Khadija.

Toward Culturally Relevant Emotion Detection Using Physiological Signals. - Ann Arbor : ProQuest Dissertations & Theses, 2020 - 56 p.

Source: Masters Abstracts International, Volume: 81-10.

Thesis (M.S.Cp.)--University of South Florida, 2020.

This item must not be sold to any third party vendors.

Research shows that emotional distress has a statistically significant impact on a student's grade point average and intent to drop out of college. Because students of different races have varying college experiences, it is important to understand the emotional experiences of different racial groups to better support students' needs and academic success. In this work, we explore several physiological responses to ten different emotional stimuli captured from 140 students. We employ unsupervised learning via the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm and supervised learning via Random Forests and Support Vector machines to analyze clustering partitions and classification performance according to emotional state (e.g., happiness), race (e.g., Asian), and all combinations of the two (e.g., happiness and Asian). We also consider a much broader scope by analyzing clustering partitions and classification results according to subject ID (i.e., biometric identification) and gender. Significant findings show that blood pressure and respiration rate provided more accurate partitions of the data with respect to emotion and race, in addition to gender. The emotional states of sadness and startled were classified with 99% and 94%, respectively, while surprise and skepticism were often misclassified as the other (i.e., surprise as skepticism and vice versa). However, in general, both unsupervised and supervised learning tasks suggested significant overlap in physiological emotional responses across the different racial groups.

ISBN: 9798607321062Subjects--Topical Terms:

523869
Computer science.
Subjects--Index Terms:

Affective computing

Toward Culturally Relevant Emotion Detection Using Physiological Signals.
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100 1 $a Zanna, Khadija. $3 3555125
245 1 0 $a Toward Culturally Relevant Emotion Detection Using Physiological Signals.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2020
300 $a 56 p.
500 $a Source: Masters Abstracts International, Volume: 81-10.
500 $a Advisor: Neal, Tempestt.
502 $a Thesis (M.S.Cp.)--University of South Florida, 2020.
506 $a This item must not be sold to any third party vendors.
520 $a Research shows that emotional distress has a statistically significant impact on a student's grade point average and intent to drop out of college. Because students of different races have varying college experiences, it is important to understand the emotional experiences of different racial groups to better support students' needs and academic success. In this work, we explore several physiological responses to ten different emotional stimuli captured from 140 students. We employ unsupervised learning via the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm and supervised learning via Random Forests and Support Vector machines to analyze clustering partitions and classification performance according to emotional state (e.g., happiness), race (e.g., Asian), and all combinations of the two (e.g., happiness and Asian). We also consider a much broader scope by analyzing clustering partitions and classification results according to subject ID (i.e., biometric identification) and gender. Significant findings show that blood pressure and respiration rate provided more accurate partitions of the data with respect to emotion and race, in addition to gender. The emotional states of sadness and startled were classified with 99% and 94%, respectively, while surprise and skepticism were often misclassified as the other (i.e., surprise as skepticism and vice versa). However, in general, both unsupervised and supervised learning tasks suggested significant overlap in physiological emotional responses across the different racial groups.
590 $a School code: 0206.
650 4 $a Computer science. $3 523869
650 4 $a Physiological psychology. $3 2144820
653 $a Affective computing
653 $a In-group advantage
653 $a Physiological data
653 $a supervised learning
653 $a unsupervised learning
690 $a 0984
690 $a 0989
710 2 $a University of South Florida. $b Computer Science and Engineering. $3 1682850
773 0 $t Masters Abstracts International $g 81-10.
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791 $a M.S.Cp.
792 $a 2020
793 $a English
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