Analysis of emotion recognition model using electroencephalogram (EEG) signals based on stimuli text

Recognizing emotions through the brain wave approach with facial or sound expression is widely used, but few use text stimuli. Therefore, this study aims to analyze the emotion recognition experiment by stimulating sentiment-tones using EEG. The process of classifying emotions uses a random forest m...

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Main Authors: Hulliyah, Khodijah, Awang Abu Bakar, Normi Sham
Format: Article
Language:English
English
Published: Turkbilmat Egitim Hizmetleri 2021
Subjects:
Online Access:http://irep.iium.edu.my/89662/13/89662_Analysis%20of%20emotion%20recognition%20model%20using%20electroencephalogram.pdf
http://irep.iium.edu.my/89662/19/89662_Analysis%20of%20emotion%20recognition%20model%20using%20electroencephalogram_SCOPUS.pdf
http://irep.iium.edu.my/89662/
https://turcomat.org/index.php/turkbilmat/article/view/910
https://doi.org/10.17762/turcomat.v12i3.910
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spelling my.iium.irep.896622021-05-04T03:27:52Z http://irep.iium.edu.my/89662/ Analysis of emotion recognition model using electroencephalogram (EEG) signals based on stimuli text Hulliyah, Khodijah Awang Abu Bakar, Normi Sham T10.5 Communication of technical information Recognizing emotions through the brain wave approach with facial or sound expression is widely used, but few use text stimuli. Therefore, this study aims to analyze the emotion recognition experiment by stimulating sentiment-tones using EEG. The process of classifying emotions uses a random forest model approach which is compared with two models, namely Support Vector Machine and decision tree as benchmarks. The raw data used comes from the results of scrapping Twitter data. The dataset of emotional annotation was carried out manually based on four classifications, specifically: happiness, sadness, fear, and anger. The annotated dataset was tested using an Electroencephalogram (EEG) device attached to the participant's head to determine the brain waves appearing after reading the text. The results showed that the random forest model has the highest accuracy level with a rate of 98% which is slightly different from the decision tree with 88%. Meanwhile, in SVM the accuracy results are less good with a rate of 32%. Furthermore, the match level of angry emotions from the three models above during manual annotation and using the EEG device showed a high number with an average value above 90%, because reading with angry expressions is easier to perform. For this reason, this study aims to test the emotion recognition experiment by stimulating sentiment-tones using EEG. The process of classifying emotions uses a random forest model approach which is compared with two models, namely SVM and decision tree as benchmarks. The dataset used comes from the results of scrapping Twitter data. Turkbilmat Egitim Hizmetleri 2021-04-05 Article PeerReviewed application/pdf en http://irep.iium.edu.my/89662/13/89662_Analysis%20of%20emotion%20recognition%20model%20using%20electroencephalogram.pdf application/pdf en http://irep.iium.edu.my/89662/19/89662_Analysis%20of%20emotion%20recognition%20model%20using%20electroencephalogram_SCOPUS.pdf Hulliyah, Khodijah and Awang Abu Bakar, Normi Sham (2021) Analysis of emotion recognition model using electroencephalogram (EEG) signals based on stimuli text. Turkish Journal of Computer and Mathematics Education (TURCOMAT), 12 (3). pp. 1384-1393. E-ISSN 1309-4653 https://turcomat.org/index.php/turkbilmat/article/view/910 https://doi.org/10.17762/turcomat.v12i3.910
institution Universiti Islam Antarabangsa Malaysia
building IIUM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider International Islamic University Malaysia
content_source IIUM Repository (IREP)
url_provider http://irep.iium.edu.my/
language English
English
topic T10.5 Communication of technical information
spellingShingle T10.5 Communication of technical information
Hulliyah, Khodijah
Awang Abu Bakar, Normi Sham
Analysis of emotion recognition model using electroencephalogram (EEG) signals based on stimuli text
description Recognizing emotions through the brain wave approach with facial or sound expression is widely used, but few use text stimuli. Therefore, this study aims to analyze the emotion recognition experiment by stimulating sentiment-tones using EEG. The process of classifying emotions uses a random forest model approach which is compared with two models, namely Support Vector Machine and decision tree as benchmarks. The raw data used comes from the results of scrapping Twitter data. The dataset of emotional annotation was carried out manually based on four classifications, specifically: happiness, sadness, fear, and anger. The annotated dataset was tested using an Electroencephalogram (EEG) device attached to the participant's head to determine the brain waves appearing after reading the text. The results showed that the random forest model has the highest accuracy level with a rate of 98% which is slightly different from the decision tree with 88%. Meanwhile, in SVM the accuracy results are less good with a rate of 32%. Furthermore, the match level of angry emotions from the three models above during manual annotation and using the EEG device showed a high number with an average value above 90%, because reading with angry expressions is easier to perform. For this reason, this study aims to test the emotion recognition experiment by stimulating sentiment-tones using EEG. The process of classifying emotions uses a random forest model approach which is compared with two models, namely SVM and decision tree as benchmarks. The dataset used comes from the results of scrapping Twitter data.
format Article
author Hulliyah, Khodijah
Awang Abu Bakar, Normi Sham
author_facet Hulliyah, Khodijah
Awang Abu Bakar, Normi Sham
author_sort Hulliyah, Khodijah
title Analysis of emotion recognition model using electroencephalogram (EEG) signals based on stimuli text
title_short Analysis of emotion recognition model using electroencephalogram (EEG) signals based on stimuli text
title_full Analysis of emotion recognition model using electroencephalogram (EEG) signals based on stimuli text
title_fullStr Analysis of emotion recognition model using electroencephalogram (EEG) signals based on stimuli text
title_full_unstemmed Analysis of emotion recognition model using electroencephalogram (EEG) signals based on stimuli text
title_sort analysis of emotion recognition model using electroencephalogram (eeg) signals based on stimuli text
publisher Turkbilmat Egitim Hizmetleri
publishDate 2021
url http://irep.iium.edu.my/89662/13/89662_Analysis%20of%20emotion%20recognition%20model%20using%20electroencephalogram.pdf
http://irep.iium.edu.my/89662/19/89662_Analysis%20of%20emotion%20recognition%20model%20using%20electroencephalogram_SCOPUS.pdf
http://irep.iium.edu.my/89662/
https://turcomat.org/index.php/turkbilmat/article/view/910
https://doi.org/10.17762/turcomat.v12i3.910
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