Abstract
Emotion prediction, the sub-domain of sentiment analysis helps to analyze the emotion. Recently, the prediction of children's behavior based on their present emotional activities is remaining as a challenging task. Henceforth, the deep learning algorithms are used to support and assist in the process of children's behavior prediction by considering the emotional features with a good accuracy rate. Besides, this article presents the prediction of children's behavior based on their emotion with the deep learning classifiers method. To analyze the performance, decision tree and naïve Bayes probability model are compared. Totally, 35 sample emotions are considered in the prediction process of deep learning classifier with a probability model. Furthermore, the hybrid emotions are incorporated in the proposed dataset. The comparison between both the decision tree and the Naïve Bayes method has been performed to predict the children's emotions after the classification. Based on the probability model of naïve Bayes method and decision tree, naïve Bayes method provides good results in terms of recognition rate and prediction accuracy when compared to the decision tree method. Therefore, a fusion of these two algorithms is proposed for predicting the emotions involved in children's behavior. This research article includes the combined algorithm mathematical proof of prediction based on the emotion samples. This article discusses about the future scope of the proposal and the obtained prediction results.
References
Pandita, A.; Sharma, D.; Pandita, D.; Pawar, S.; Tariq, M.; Kaul, A. Childhood Obesity: Prevention is better than Cure. Available online: https://www.dovepress.com/childhood-obesity-prevention-is-better-than-curepeer-reviewed-article-DMSO (June 2020).
Fox, C.K.; Ryder, J.R.; Gross, A.C.; Kelly, A.S. Obesity in children and adolescents. In Obesity; Sbraccia, P., Finer, N., Eds.; Endocrinology; Springer International Publishing: Cham, Switzerland, 2019; pp. 295–322.[CrossRef]
Anderson, Y.C.;Wynter, L.E.; Treves, K.F.; Grant, C.C.; Stewart, J.M.; Cave, T.L.;Wouldes, T.A.; Derraik, J.G.B.; Cutfield, W.S.; Hofman, P.L. Assessment of health-related quality of life and psychological well-being of children and adolescents with obesity enrolled in a New Zealand community-based intervention programme: An observational study. BMJ Open 2017, 7, e015776.[CrossRef][PubMed]
S. A. Morsy and A. Rafea, “Improving document-level sentiment classification using contextual valence shifters”, In Lecture Notes in Computer Science, No. 7337, 2012, pp.253-258.
R. A. Calvo and S. M. Kim, “Emotions in text: dimensional and categorical models”, Computational Intelligence, vol. 29 no. 3, 2013, pp.527-543.
K. Koroveshovski and S. Gievska, “Someone to talk to”, In Proceedings of the Fifth Internation Conference on Applied Human Factors and Ergonomics AHFE 2014, vol. 19, 2014.
D. Markovikj, S. Gievska, D.S. Stillwell, and M. Kosinski, “Mining facebook data for predictive personality modeling”, In Proceedings of WCPR’13 (Shared Task), in conjunction with the Seventh international Conference on Weblogs and Social Media ICWSM’13, AAAI Press, 2013.
S. Gievska and K. Koroveshovski, “The impact of affective verbal content on predicting personality impressions in YouTube videos”, In Proceedings of the Workshop on Computational Personality Prediction 2014, Shared Task, in conjusction with ACM Multimedia 2014. (in press)
J-I. Biel and D. Gatica-Perez, “The YouTube lens: Crowdsourced personality impressions and audiovisual analysis of Vlogs. IEEE Transactions on Multimedia, vol.15, no. 1, 2013, pp. 41-55.
M. Wöllmer, B. Schuller, F. Eyben and G. Rigoll, Combining Long Short-Term Memory and Dynamic Bayesian Networks for Incremental Emotion-Sensitive Artificial Listening, In: IEEE Journal of Selected Topics in Signal Processing, vol. 4, 2010, pp.867-881.
Dada EG, Bassi JS, Chiroma H, Abdulhamid SM, Adetunmbi AO, Ajibuwa OE. Machine learning for email spam filtering: review, approaches and open research problems. Heliyon 2019;5(6):e01802. https://doi.org/10.1016/j.heliyon.2019. e01802.
Xie M. Development of artificial intelligence and effects on financial system. J Phys Conf 2019;1187:032084. https://doi.org/10.1088/1742-6596/1187/3/032084.
Hegazy O, Soliman OS, Salam MA. A machine learning model for stock market prediction. Int J Comput Sci Telecommun 2014;4(12):16–23.
Beckmann JS, Lew D. Reconciling evidence-based medicine and precision medicine in the era of big data: challenges and opportunities. Genome Med 2016;8(1):134–9.
Quinlan J.R., 1986. Induction on decision trees, Machine learning, 1(1):81-106.
C. Clark and A. J. Storkey. Training deep convolutional neural networks to play go. In Proceedings of the 32nd International Conference on Machine Learning, ICML 2015, 2015.
Masuyama, Eitaro., "A number of fundamental emotions and their definitions”, Robot and Human Communication, RO-MAN '94 Nagoya, Proceedings of the 3rd IEEE International Workshop, 156-161, (1994)
D. Silver, A. Huang, C. J. Maddison, A. Guez, L. Sifre, G. van den Driessche, J. Schrittwieser, I. Antonoglou, V. Panneershelvam, M. Lanctot, S. Dieleman, Grewe D, J. Nham, N. Kalchbrenner, I. Sutskever, T. Lillicrap, M. Leach, K. Kavukcuoglu, T. Graepel, and D. Hassabis. Mastering the game of go with deep neural networks and tree search. Nature, 529, 2016.
Chuang, Ze-Jing & Wu, Chung-Hsien, "Multimodal Emotion Recognition from Speech and Text", Computational Linguistics and Chinese Language Processing, Vol. 9, No. 2, pp. 45-62, August, (2004).
M. Gera and S. Goel, “A model for predicting the eligibility for placement of students using data mining technique," International Conference on Computing, Communication and Automation, vol. 4, pp. 18-23, January 2015.
Rankin, J.; Matthews, L.; Cobley, S.; Han, A.; Sanders, R.; Wiltshire, H.D.; Baker, J.S. Psychological Consequences of Childhood Obesity: Psychiatric Comorbidity and Prevention. Available online: https://www.dovepress.com/psychological--consequences-of-childhood-obesity-psychiatric-comorbidipeer-reviewed-article-AHMT (June 2020).
Abdullah M, Hadzikadicy M, Shaikhz S. SEDAT: sentiment and emotion detection in Arabic text using CNN-LSTM deep learning. Paper presented at: Proceedings of the 2018 17th IEEE International Conference on Machine Learning and Applications; 2018:835-840; IEEE.
Chatterjee A, Gupta U, Chinnakotla MK, Srikanth R, Galley M, Agrawal P. Understanding emotions in text using deep learning and big data. Comput Hum Behav. 2019;93:309-317.
Weber GM, Mandl KD, Kohane IS. Finding the missing link for big biomedical data. Jama 2014;311(24):2479–80.
Loconsole C, Chiaradia D, Bevilacqua V, Frisoli A. Real-time emotion recognition: an improved hybrid approach for classification performance. Intelligent Computing Theory 2014:320–31.
Huang X, Kortelainen J, Zhao G, Li X, Moilanen A, Sepp€anen T, Pietik€ainen M. Multi-modal emotion analysis from facial expressions and electroencephalogram. Compute Vis Image Understand 2016;147:114–24. https://doi.org/10.1016/j. cviu.2015.09.015.
Bhattacharya J, Lindsen JP. Music for a brighter world: brightness judgment bias by musical emotion. PloS One 2016;11(2):e0148959. https://doi.org/10.1371/ journal.pone.0148959.
Sangaiah AK, Arumugam M, Bian G-B. An intelligent learning approach for improving ECG signal classification and arrhythmia analysis. Artif Intell Med 2019: 101788. https://doi.org/10.1016/j.artmed.2019.101788.