Emotion Detection for Health and Well-being in Short Messaging Systems

Emeka Ogbuju

doi:10.12928/si.v21i2.139

Authors

Emeka Ogbuju Federal University Lokoja

DOI:

https://doi.org/10.12928/si.v21i2.139

Keywords:

Emotions Detection, Short Message Service, Logistic Regression, Machine Learning, Text Analysis

Abstract

The exposure to unpleasant emotions or content in messages can lead to health complications, including high blood pressure and several heart-related disorders. Hence, the identification of unpleasant emotions in written content can serve as a beneficial instrument in addressing certain health-related issues. Emotions can be communicated through diverse modalities, including written text, spoken language, and facial gestures. The objective of this work is to create a Text-based emotion detection system that possesses the capability to accurately identify emotions within text messages. The use of message filtering mechanisms that detect and block content containing negative emotions can serve as a preventive measure to shield users from accessing messages that have the potential to adversely impact their well-being. Conversely, messages that convey positive or neutral emotions remain accessible for comprehension. In order to accomplish this objective, a combination of three machine learning algorithms, namely Naive Bayes, Support Vector Machine, and Logistic Regression, were employed, adhering to the CRISP-DM approach. The Logistic Regression technique achieved the greatest accuracy rate of 98.4% and was employed in the construction of the detection system. The Graphical User Interface (GUI) of the system was developed utilizing HTML and CSS, with the integration of diverse components to establish a comprehensive and operational interface for the user.

Author Biography

Emeka Ogbuju, Federal University Lokoja

Department of Computer Science

References

Adebowale, M. A., Lwin, K. T., Sánchez, E., & Hossain, M. A. (2019). Intelligent web-phishing detection and protection scheme using integrated features of Images, frames and text. Expert Systems with Applications, 115, 300–313. https://doi.org/10.1016/j.eswa.2018.07.067

Adiyanto, O., Mohamad, E., Jaafar, R., & Faishal, M. (2023). Identification of Musculoskeletal Disorder among Eco-Brick Workers in Indonesia. International Journal of Occupational Safety and Health, 13(1), 29-40. https://doi.org/10.3126/ijosh.v13i1.44575

Anusha, V., & Sandhya, B. (2015). A learning-based emotion classifier with semantic text processing. In: El-Alfy, ES., Thampi, S., Takagi, H., Piramuthu, S., Hanne, T. (eds) Advances in Intelligent Informatics. Advances in Intelligent Systems and Computing, 320. Springer, Cham. https://doi.org/10.1007/978-3-319-11218-3_34

Byun, H. W., & Lee, J. S. (2010). Emotion-based gesture stylization for animated SMS. Journal of Korea Multimedia Society, 13(5), 802-816.

Cernea, D., & Kerren, A. (2015). A survey of technologies on the rise for emotion-enhanced interaction. Journal of Visual Languages & Computing, 31, 70–86. https://doi.org/10.1016/j.jvlc.2015.10.001

Chen, S., Webb, G. I., Liu, L., & Ma, X. (2020). A novel selective naïve Bayes algorithm. Knowledge-Based Systems, 192, 105361. https://doi.org/10.1016/j.knosys.2019.105361

Christ, M., Braun, N., Neuffer, J., & Kempa-Liehr, A. W. (2018). Time Series FeatuRe Extraction on basis of Scalable Hypothesis tests (tsfresh – A Python package). Neurocomputing, 307, 72–77. https://doi.org/10.1016/j.neucom.2018.03.067

Danisman, T., & Alpkocak, A. (2008, April). Feeler: Emotion classification of text using vector space model. In AISB 2008 convention communication, interaction and social intelligence, 1(4): 53-59

Emeka, O., Taiwo, A., Francisca, O. (2023). Text analytics solutions for the control of fake news: Materials and methods. International Journal of Open Information Technologies, 11(3): 69-74

Herath, U., Tavadze, P., He, X., Bousquet, E., Singh, S., Munoz, F., & Romero, A. H. (2020). PyProcar: A Python library for electronic structure pre/post-processing. Computer Physics Communications, 251, 107080. https://doi.org/10.1016/j.cpc.2019.107080

Ho, D.T., & Cao, T.H. (2012). A high-order hidden Markov model for emotion detection from textual data. In: Richards, D., Kang, B.H. (eds) Knowledge Management and Acquisition for Intelligent Systems. PKAW 2012. Lecture Notes in Computer Science, 7457. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-32541-0_8

Imtyaz, A., Abid Haleem, & Javaid, M. (2020). Analysing governmental response to the COVID-19 pandemic. Journal of Oral Biology and Craniofacial Research, 10(4), 504–513. https://doi.org/10.1016/j.jobcr.2020.08.005

Jain, V. K., Kumar, S., & Fernandes, S. L. (2017). Extraction of emotions from multilingual text using intelligent text processing and computational linguistics. Journal of computational science, 21, 316-326.

Khamparia, A., Singh, A., Luhach, A. Kr., Pandey, B., & Pandey, D. K. (2020). Classification and Identification of Primitive Kharif Crops using Supervised Deep Convolutional Networks. Sustainable Computing: Informatics and Systems, 28, 100340. https://doi.org/10.1016/j.suscom.2019.07.003

Krishnan, H., Elayidom, M. S., & Santhanakrishnan, T. (2017). Emotion detection of tweets using Naïve Bayes classifier. International Journal of Engineering Technology Science and Research, 4(11), 457-462.

Marins, M. A., Barros, B. D., Santos, I. H., Barrionuevo, D. C., Vargas, R. E. V., de M. Prego, T., de Lima, A. A., de Campos, M. L. R., da Silva, E. A. B., & Netto, S. L. (2021). Fault detection and classification in oil wells and production/service lines using random forest. Journal of Petroleum Science and Engineering, 197, 107879. https://doi.org/10.1016/j.petrol.2020.107879

Nasiboglu, R., & Nasibov, E. (2022). FyzzyGBR A gradient boosting regression software with fuzzy target values. Software Impacts, 14, 100430. https://doi.org/10.1016/j.simpa.2022.100430

Neviarouskaya, A., Prendinger, H., & Ishizuka, M. (2010). EmoHeart: conveying emotions in second life based on affect sensing from text. Advances in Human-Computer Interaction, 1.

Ogbuju, E., Yemi-Peters, V., Osumah, T., Agbogun, J., Ejiofor, V. (2021). A frontpage online news analysis: An opinion mining approach. Journal of Computer Science and Its Application, 28(1): 107-120. https://doi.org/10.4314/jcsia.v28i1.9

Ogbuju, E., Mpama, I., Oluwafemi, T.M., Ochepa, F.O., Agbogun, J (2022). The sentiment analysis of EndSARS protest in Nigeria. Journal of Applied Artificial Intelligence, 3(2): 13-23

Oeldorf-Hirsch, A., & Sundar, S. S. (2015). Posting, commenting, and tagging: Effects of sharing news stories on Facebook. Computers in Human Behavior, 44, 240–249. https://doi.org/10.1016/j.chb.2014.11.024

Shalaby, R., Adu, M. K., El Gindi, H. M., & Agyapong, V. I. O. (2022). Text Messages in the field of mental health: Rapid review of the reviews. Frontiers in Psychiatry, 13, 921982. https://doi.org/10.3389/fpsyt.2022.921982

Shivhare, S. N., & Khethawat, S. (2012). Emotion detection from text. https://doi.org/10.48550/arXiv.1205.4944

Stephens-Fripp, B. (2016). Combining Local and Global Features in Automatic Affect Recognition from Body Posture and Gait. University of Wollongong Thesis Collection 1954-2016. https://ro.uow.edu.au/theses/4923

Wu, D., Moody, G. D., Zhang, J., & Lowry, P. B. (2020). Effects of the design of mobile security notifications and mobile app usability on users’ security perceptions and continued use intention. Information & Management, 57(5), 103235. https://doi.org/10.1016/j.im.2019.103235

Xu, J., Kang, Q., Song, Z., & Clarke, C. P. (2015). Applications of Mobile Social Media: WeChat Among Academic Libraries in China. The Journal of Academic Librarianship, 41(1), 21–30. https://doi.org/10.1016/j.acalib.2014.10.012

Yadav, R., & Raheman, H. (2023). Development of an artificial neural network model with graphical user interface for predicting contact area of bias-ply tractor tyres on firm surface. Journal of Terramechanics, 107, 1–11. https://doi.org/10.1016/j.jterra.2023.01.004