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Non-acted text and keystrokes database and learning methods to recognize emotions

Non-acted text and keystrokes database and learning methods to recognize emotions
Non-acted text and keystrokes database and learning methods to recognize emotions
The modern computing applications are presently adapting to the convenient availability of huge and diverse data for making their pattern recognition methods smarter. Identification of dominant emotion solely based on the text data generated by the humans is essential for the modern human-computer interaction. This work presents a multimodal text-keystrokes dataset and associated learning methods for the identification of human emotions hidden in small text. For this, a text-keystrokes data of 69 participants is collected in multiple scenarios. Stimuli are induced through videos in a controlled environment. After the stimuli induction, participants write their reviews about the given scenario in an unguided manner. Afterward, keystroke and in-text features are extracted from the dataset. These are used with an assortment of learning methods to identify emotion hidden in the short text. An accuracy of 86.95% is achieved by fusing text and keystroke features. Whereas, 100% accuracy is obtained for pleasure-displeasure classes of emotions using the fusion of keystroke/text features, tree-based feature selection method, and support vector machine classifier. The present work is also compared with four state-of-the-art techniques for the same task, where the results suggest that the present proposal performs better in terms of accuracy.
1551-6857
1-24
Tahir, Madiha
9e351fce-fbac-41ca-920f-e4916f79d99b
Halim, Zahid
4c6555ce-bf70-48d1-9b0c-2172ba5f22d3
Rahman, Atta Ur
6bddc014-3e3b-4e3a-ba6e-ee129250c1cc
Waqas, Muhammad
b487e458-27ab-44dd-be5d-f785ba9e50fd
Tu, Shanshan
ef946f84-9863-4438-a847-0171915b0651
Chen, Sheng
9310a111-f79a-48b8-98c7-383ca93cbb80
Han, Zhu
8909f296-f05d-4cde-9e02-402c64a7e87e
Tahir, Madiha
9e351fce-fbac-41ca-920f-e4916f79d99b
Halim, Zahid
4c6555ce-bf70-48d1-9b0c-2172ba5f22d3
Rahman, Atta Ur
6bddc014-3e3b-4e3a-ba6e-ee129250c1cc
Waqas, Muhammad
b487e458-27ab-44dd-be5d-f785ba9e50fd
Tu, Shanshan
ef946f84-9863-4438-a847-0171915b0651
Chen, Sheng
9310a111-f79a-48b8-98c7-383ca93cbb80
Han, Zhu
8909f296-f05d-4cde-9e02-402c64a7e87e

Tahir, Madiha, Halim, Zahid, Rahman, Atta Ur, Waqas, Muhammad, Tu, Shanshan, Chen, Sheng and Han, Zhu (2022) Non-acted text and keystrokes database and learning methods to recognize emotions. ACM Transactions on Multimedia Computing, Communications, and Applications, 18 (2), 1-24.

Record type: Article

Abstract

The modern computing applications are presently adapting to the convenient availability of huge and diverse data for making their pattern recognition methods smarter. Identification of dominant emotion solely based on the text data generated by the humans is essential for the modern human-computer interaction. This work presents a multimodal text-keystrokes dataset and associated learning methods for the identification of human emotions hidden in small text. For this, a text-keystrokes data of 69 participants is collected in multiple scenarios. Stimuli are induced through videos in a controlled environment. After the stimuli induction, participants write their reviews about the given scenario in an unguided manner. Afterward, keystroke and in-text features are extracted from the dataset. These are used with an assortment of learning methods to identify emotion hidden in the short text. An accuracy of 86.95% is achieved by fusing text and keystroke features. Whereas, 100% accuracy is obtained for pleasure-displeasure classes of emotions using the fusion of keystroke/text features, tree-based feature selection method, and support vector machine classifier. The present work is also compared with four state-of-the-art techniques for the same task, where the results suggest that the present proposal performs better in terms of accuracy.

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4. MTP 1.14-ACM-R1.3-Final file - Accepted Manuscript
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Accepted/In Press date: 10 August 2021
Published date: 17 February 2022

Identifiers

Local EPrints ID: 451318
URI: http://eprints.soton.ac.uk/id/eprint/451318
ISSN: 1551-6857
PURE UUID: 681079f1-747f-4f78-a69f-e28637fd3b9c

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Date deposited: 21 Sep 2021 16:30
Last modified: 29 Apr 2022 16:32

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Contributors

Author: Madiha Tahir
Author: Zahid Halim
Author: Atta Ur Rahman
Author: Muhammad Waqas
Author: Shanshan Tu
Author: Sheng Chen
Author: Zhu Han

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