A Hybrid Model for Human Behavior Recognition Using Emotions, Sentiments, and Mood Features
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Keywords

Social Networks, Social Trust, Positive and Negative state log, Mathematical framework, Hybrid tactic, Multinomial classification, Human behavior, Natural Language Processing
Social Networks
Social Trust
Positive and Negative state log
Mathematical framework
Hybrid tactic
Multinomial classification
Human behavior
Natural Language Processing

How to Cite

Shakir, H., Samreen, A. ., Ali, S. A., & Hussain, M. (2024). A Hybrid Model for Human Behavior Recognition Using Emotions, Sentiments, and Mood Features. KIET Journal of Computing and Information Sciences, 7(1). https://doi.org/10.51153/kjcis.v7i1.214

Abstract

While social networking is a powerful communication tool, the obscured behavior of individuals on social networks remains a significant problem for users. Currently, research work is being focused on formulating mechanisms to determine the obscured behavior of users for secure and trustworthy social media. The proposed model employs mathematical formulation and multinomial classification of mood and emotions to analyze the conduct of an individual, thus enabling social trust on social media. First, natural language processing techniques are applied to predict the emotions, moods, and sentiments of an individual from the text, and then a mathematical model is applied to gather a comprehensive picture of one’s behavior using calculations at numerous instants. Finally, a subsequent trust state log is built in terms of positive and negative states which show the devotion in behavior in terms of mood, sentiments, and more significantly emotions. The efficiency of the proposed work has been demonstrated using simulation-based and real-world datasets along with individual behavior graphs for various conversations

https://doi.org/10.51153/kjcis.v7i1.214
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References

Ajzen, I., &Fishbein, M. (1977). Attitude-behavior relations: A theoretical analysis and review of empirical research. Psychological bulletin, 84(5), 888.

Almeida, A., &Azkune, G. (2018).Predicting human behavior with recurrent neural networks.Applied Sciences, 8(2), 305.

Al-Samadi,M.,Qawasmeh,O.,Al-Ayyoub,M.,Jararweh,Y., &Gupta,B.(2018).Deep Recurrent neural network vs. support vector machine for aspect-based sentiment analysis of Arabic hotels’ review. Journal of computational science , 27,386-393.

Alzhrani, K. M. (2022). Political Ideology Detection of News Articles Using Deep Neural Networks.Intelligent Automation & Soft Computing, 33(1).

Balaji, B. S., Balakrishnan, S., Venkatachalam, K., &Jeyakrishnan, V. (2021). Automated query classification based web service similarity technique using machine learning. Journal of Ambient Intelligence and Humanized Computing, 12, 6169-6180.

Bennett, D., Davidson, G., &Niv, Y. (2022).A model of mood as integrated advantage.Psychological Review, 129(3), 513.

Brass, D. J., Butterfield, K. D., & Skaggs, B. C. (1998). Relationships and unethical behavior: A social network perspective. Academy of management review, 23(1), 14-31.

Deng, S., Xia, S., Hu, J., Li, H., & Liu, Y. (2021).Exploring the topic structure and evolution of associations in information behavior research through co-word analysis. Journal of Librarianship and Information Science, 53(2), 280-297.

Dinesen, P. T., Schaeffer, M., &Sønderskov, K. M. (2020). Ethnic diversity and social trust: A narrative and meta-analytical review. Annual Review of Political Science, 23, 441-465.

Fennell, P. G., Zuo, Z., &Lerman, K. (2019). Predicting and explaining behavioral data with structured feature space decomposition. EPJ Data Science, 8(1), 1-27.

Gross, J. J. (1998). The emerging field of emotion regulation: An integrative review. Review of general psychology, 2(3), 271-299.

Gutierrez, E., Karwowski, W., Fiok, K., Davahli, M. R., Liciaga, T., &Ahram, T. (2021). Analysis of human behavior by mining textual data: current research topics and analytical techniques. Symmetry, 13(7), 1276.

Russell, J. A. (1980). A circumplex model of affect.Journal of personality and social psychology, 39(6), 1161.

Kaji?, I., Schröder, T., Stewart, T. C., &Thagard, P. (2019). The semantic pointer theory of emotion: Integrating physiology, appraisal, and construction. Cognitive Systems Research, 58, 35-53.

Kim, S. H., & Kim, S. (2021). Social trust as an individual characteristic or societal property?. International Review of Public Administration, 26(1), 1-17.

Kolliakou, A., Bakolis, I., Chandran, D., Derczynski, L., Werbeloff, N., Osborn, D. P., ...& Stewart, R. (2020). Mental health-related conversations on social media and crisis episodes: a time-series regression analysis. Scientific Reports, 10(1), 1342.

Leary, M. (2012).Understanding the Mysteries of Human Behavior.Great Courses.

Mao, X., Chang, S., Shi, J., Li, F., & Shi, R. (2019).Sentiment-aware word embedding for emotion classification.Applied Sciences, 9(7), 1334.

Pentland, A., & Liu, A. (1999).Modeling and prediction of human behavior.Neural computation, 11(1), 229-242.

Plonsky, O., Apel, R., Ert, E., Tennenholtz, M., Bourgin, D., Peterson, J. C., ...&Erev, I. (2019). Predicting human decisions with behavioral theories and machine learning.arXiv preprint arXiv:1904.06866.

R?czy, K., &Orzechowski, J. (2021). When working memory is in a mood: Combined effects of induced affect and processing of emotional words. Current Psychology, 40, 2843-2852.

Riedl, M. O. (2019). Human?centered artificial intelligence and machine learning.Human Behavior and Emerging Technologies, 1(1), 33-36.

Satu, M. S., Khan, M. I., Mahmud, M., Uddin, S., Summers, M. A., Quinn, J. M., &Moni, M. A. (2021). TClustVID: A novel machine learning classification model to investigate topics and sentiment in COVID-19 tweets. Knowledge-Based Systems, 226, 107126.

Sheeran, P., & Webb, T. L. (2016).The intention–behavior gap.Social and personality psychology compass, 10(9), 503-518.

Torre, J. B., & Lieberman, M. D. (2018). Putting feelings into words: Affect labeling as implicit emotion regulation. Emotion Review, 10(2), 116-124.

Yu, Z., Du, H., Yi, F., Wang, Z., &Guo, B. (2019).Ten scientific problems in human behavior understanding.CCF Transactions on Pervasive Computing and Interaction, 1, 3-9.

Emanuel, A., & Eldar, E. (2023). Emotions as computations. Neuroscience & Biobehavioral Reviews, 144, 104977.

Machová, K., Szabóova, M., Parali?, J., & Mi?ko, J. (2023). Detection of emotion by text analysis using machine learning. Frontiers in Psychology, 14, 1190326.