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Application of Affective Computing in the Analysis of Emotions of Educational Content for the Prevention of COVID-19


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DOI: https://doi.org/10.15866/irea.v10i3.21056

Abstract


Affective computing is an emerging research area focused on the development of devices and systems that have the ability to recognize, interpret, process and simulate human emotions in order to improve a user's experience when interacting with a software system. One of the possible fields of application of the techniques provided by affective computing is in the design and generation of multimedia content in the context of formal and non-formal education, which can generate greater interest in students through the transmission of different emotions throughout this content. Based on the above, in this article, an analysis of emotions is carried out on a set of content provided by the Ministry of Health of Colombia as a measure for the prevention and mitigation of contagion by COVID-19. For the development of the study, a tool has been built in the Java language, which allows the segmentation of the audio fragments of the multimedia content, as well as the extraction of the acoustic parameters of arousal and valence, and the application of clustering models on the set of properties extracted from the segments.
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Keywords


Analysis of Emotions; Affective Computing; Arousal; Valence; Clustering

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References


I. Alemán, E. Vera, and M. J. Patiño-Torres, "COVID-19 and medical education: Challenges and opportunities in Venezuela," Educ. Medica, 2020.
https://doi.org/10.1016/j.edumed.2020.06.005

C. Xiao and Y. Li, "Analysis on the Influence of the Epidemic on the Education in China," in 2020 International Conference on Big Data and Informatization Education (ICBDIE), Jul. 2020, pp. 143-147.
https://doi.org/10.1109/ICBDIE50010.2020.00040

R. R. Vásquez-Sullca, "Remote education in physicians resident in covid-19 times," Medical Education. 2020.
https://doi.org/10.1016/j.edumed.2020.05.006

Y. Chtouki, H. Harroud, M. Khalidi and S. Bennani, "The impact of YouTube videos on the student's learning," 2012 International Conference on Information Technology Based Higher Education and Training (ITHET), 2012, pp. 1-4.
https://doi.org/10.1109/ITHET.2012.6246045

M. I. Ramírez-Ochoa, "Possibilities of the educational use of youtube," Rev. Ra Ximhai, vol. 12, no. 6, pp. 537-546, 2016.
https://doi.org/10.35197/rx.12.01.e3.2016.34.mr

I. Y. Shehu, U. Abubakar, A. M. Kawu and B. Sa'idu, "Effect of Youtube-Video Embedded Instruction on Students' Academic Achievement In Automotive Technology Education In Tertiary Institutions of North-Eastern Nigeria," 2019 2nd International Conference of the IEEE Nigeria Computer Chapter (NigeriaComputConf), 2019, pp. 1-4.
https://doi.org/10.1109/NigeriaComputConf45974.2019.8949616

B. Xu, Y. Fu, Y. G. Jiang, B. Li, and L. Sigal, "Heterogeneous knowledge transfer in video emotion recognition, attribution and summarization," IEEE Trans. Affect. Comput., 2018.
https://doi.org/10.1109/TAFFC.2016.2622690

I. Martín de Diego, A. Serrano, C. Conde García, and E. Cabello, "Automatic emotion recognition techniques," Educ. Knowl. Soc., 2006.

Calvo, R. A. and S. D'Mello, "Affect detection: an interdisciplinary review of models, methods, and their applications," IEEE Trans. Affect. Comput., vol. 1, pp. 18-37, 2010.
https://doi.org/10.1109/T-AFFC.2010.1

R. W. Picard, Affective Computing. Cambridge: MA: MIT press., 1997.
https://doi.org/10.1037/e526112012-054

Z. Zhi and H. Jinde, "Emotion Computing Method Based on Knowledge Representation," in Proceedings - 2020 International Conference on Computer Engineering and Application, ICCEA 2020, Mar. 2020, pp. 368-372.
https://doi.org/10.1109/ICCEA50009.2020.00086

S. Baldasarri, "Affective Computing: technology and emotions to improve the user experience," J. Inst. la Fac. Informatics, no. 3, pp. 14-15, 2016.

J. A. Russell, "A circumplex model of affect.," J. Pers. Soc. Psychol., vol. 39, no. 6, pp. 1161-1178, 1980.
https://doi.org/10.1037/h0077714

L. A. Solarte Moncayo, M. Sánchez Barragán, G. E. Chanchí Golondrino, D. F. Durán Dorado, and J. L. Arciniegas Herrera, "Video on demand service based on inference emotions user," Sist. and Telematics, 2016.doi: 10.18046/syt.v14i38.2286
https://doi.org/10.18046/syt.v14i38.2286

F. H. Rachman, R. Samo and C. Fatichah, "Song Emotion Detection Based on Arousal-Valence from Audio and Lyrics Using Rule Based Method," 2019 3rd International Conference on Informatics and Computational Sciences (ICICoS), 2019, pp. 1-5.
https://doi.org/10.1109/ICICoS48119.2019.8982519

Y. Li and H. Wu, "A Clustering Method Based on K-Means Algorithm," Phys. Procedia, 2012.
https://doi.org/10.1016/j.phpro.2012.03.206

K. S. Pratt, "Design Patterns for Research Methods: Iterative Field Research," AAAI Spring Symp. Exp. Des. Real, 2009.

Y. E. Kim et al., "Music emotion recognition: A state of the art review," 255 11th International Society for Music Information Retrieval Conference (ISMIR 2010), 2010.

E. Schubert, "Update of the Hevner Adjective Checklist," Percept. Mot. Skills, 2003.
https://doi.org/10.2466/PMS.96.4.1117-1122

X. Hu, J. S. Downie, C. Laurier, M. Bay, and A. F. Ehmann, "The 2007 mirex audio mood classification task: Lessons learned," ISMIR 2008 - Session 4a - Data Exchange, Archiving and Evaluation, 2008.

R. E. Thayer, "Modern Perspectives on Mood," in The Biopsychology of Mood and Arousal, 1989.

Y. H. Yang and H. H. Chen, "Machine recognition of music emotion: A review," ACM Transactions on Intelligent Systems and Technology. 2012.doi: 10.1145/2168752.2168754
https://doi.org/10.1145/2168752.2168754

A. Kartikay, H. Ganesan and V. M. Ladwani, "Classification of music into moods using musical features," 2016 International Conference on Inventive Computation Technologies (ICICT), 2016, pp. 1-5.
https://doi.org/10.1109/INVENTIVE.2016.7830197

J. Kim, S. Lee, S. Kim and W. Y. Yoo, "Music mood classification model based on arousal-valence values," 13th International Conference on Advanced Communication Technology (ICACT2011), 2011, pp. 292-295.

B. Gopal Patra, D. Das, and S. Bandyopadhyay, "Automatic Music Mood Classification of Hindi Songs," in 3rd Workshop on Sentiment Analysis where AI meets Psychology (SAAIP 2013), 2013, pp. 24-28.

S. Lekamge, A. Marashinghe, P. Kalansooriya, and S. Nomura, "A Visual Interface for Emotion based Music Navigation using Subjective and Objective Measures of Emotion Perception," Int. J. Affect. Eng., 2016.
https://doi.org/10.5057/ijae.IJAE-D-15-00039

A. Aljanaki, Y. H. Yang, and M. Soleymani, "Developing a benchmark for emotional analysis of music," PLoS One, 2017.doi: 10.1371/journal.pone.0173392
https://doi.org/10.1371/journal.pone.0173392

J. Grekow, "Audio features dedicated to the detection of arousal and valence in music recordings," 2017 IEEE International Conference on INnovations in Intelligent SysTems and Applications (INISTA), 2017, pp. 40-44.
https://doi.org/10.1109/INISTA.2017.8001129

K. M. H. Abeyratne and K. L. Jayaratne, "Classification of Sinhala Songs based on Emotions," in 2019 International Conference on Advances in ICT for Emerging Regions (ICTer), 2019, pp. 1-10.
https://doi.org/10.1109/ICTer48817.2019.9023756

I. H. Witten, E. Frank, M. A. Hall, and C. J. Pal, Data Mining: Practical Machine Learning Tools and Techniques, 2016.
https://doi.org/10.1016/B978-0-12-804291-5.00010-6

S. Cunningham, H. Ridley, J. Weinel, and R. Picking, "Audio emotion recognition using machine learning to support sound design," in 14th International Audio Mostly Conference: A Journey in Sound, AM 2019, 2019, pp. 116-123.
https://doi.org/10.1145/3356590.3356609

C. Joseph and S. Lekamge, "Machine Learning Approaches for Emotion Classification of Music: A Systematic Literature Review," 2019 International Conference on Advancements in Computing (ICAC), 2019, pp. 334-339.
https://doi.org/10.1109/ICAC49085.2019.9103378

G. E. Chanchí Golondrino and A. E. Cordoba, "Analysis of emotions and sentiments on the discourse of the signing of the peace agreement in Colombia," Rev. Ibérica Sist. e Tecnol. Informação, no. E22, pp. 95-107, 2019.

J. Posner, J. A. Russell, and B. S. Peterson, "The circumplex model of affect: An integrative approach to affective neuroscience, cognitive development, and psychopathology," Dev. Psychopathol., vol. 17, no. 3, pp. 715-734, Jul. 2005.
https://doi.org/10.1017/S0954579405050340

L. A. Solarte Moncayo, M. Sánchez Barragán, G. E. Chanchí Golondrino, D. F. Duran Dorado, and J. L. Arciniegas Herrera, "Dataset of music video content, based on emotions," Eng. USBMed, 2016.
https://doi.org/10.21500/20275846.2460

F. Eyben, M. Wöllmer and B. Schuller, "OpenEAR - Introducing the munich open-source emotion and affect recognition toolkit," 2009 3rd International Conference on Affective Computing and Intelligent Interaction and Workshops, 2009, pp. 1-6.
https://doi.org/10.1109/ACII.2009.5349350


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