[Acharya, A., Ramesh, R., Fathima, T. and Lakhani, T. (2023). Clinical tools to detect postpartum depression based on machine learning and EEG: A review, in 2nd International Conference on Computational Systems and Communication (ICCSC), Thiruvananthapuram, India, pp. 1–8.]Search in Google Scholar
[Ahmed, M., Kashem, M.A., Rahman, M. and Khatun, S. (2020). Review and analysis of risk factor of maternal health in remote area using the internet of things (IoT), in A.N. Kasruddin Nasir et al. (Eds), InECCE2019, Lecture Notes in Electrical Engineering, Vol. 632, Springer, Singapore, pp. 357–365.]Search in Google Scholar
[Ahmed, M. and Kashem, M.A. (2020). IoT based risk level prediction model for maternal health care in the context of Bangladesh, Proceedings of the 2nd International Conference on Sustainable Technologies for Industry 4.0 (STI), Dhaka, Bangladesh, pp. 1–6, DOI: 10.1109/STI50764.2020.9350320.]Search in Google Scholar
[Afyouni, I., Al Aghbari, Z. and Razack, R.A. (2022). Multi-feature, multimodal, and multi-source social event detection: A comprehensive survey, Information Fusion 79: 279–308, DOI: 10.1016/j.inffus.2021.10.013.]Search in Google Scholar
[Argyriou, A., Evgeniou, T. and Pontil, M. (2008). Convex multi-task feature learning, Machine Learning 73(3): 243–272.]Search in Google Scholar
[Byeon, H. (2023). Advances in machine learning and explainable artificial intelligence for depression prediction, International Journal of Advanced Computer Science and Applications 14(6): 896, DOI: 10.14569/IJACSA.2023.0140656.]Search in Google Scholar
[Cimtay, Y., Ekmekcioglu, E. and Caglar-Ozhan, S. (2020). Cross-subject multimodal emotion recognition based on hybrid fusion, IEEE Access 8: 168865–168878, DOI: 10.14569/IJACSA.2023.0140656.]Search in Google Scholar
[Fazeli, S. (2024). Heartbeat data, https://www.kaggle.com/datasets/shayanfazeli/heartbeat.]Search in Google Scholar
[García-Díaz, J.A., Cánovas-García, M. and Valencia-García, R. (2020). Ontology-driven aspect-based sentiment analysis classification: An infodemiological case study regarding infectious diseases in Latin America, Future Generation Computer Systems 112: 641–657, DOI: 10.1016/j.future.2020.06.019.]Search in Google Scholar
[Geethanjali, R. and Valarmathi, A. (2022). Issues and future challenges of sentiment analysis for social networks—A survey, 2022 International Conference on Automation, Computing and Renewable Systems (ICACRS), Pudukkottai, India, pp. 332–339.]Search in Google Scholar
[Geethanjali, R. (2024). Maternal health risk data, https://www.kaggle.com/datasets/geethanjaliyokesh/maternal-health-risk-data-1.]Search in Google Scholar
[Ghosh, T., Banna, M.H.A., Nahian, M.J.A., Uddin, M.N., Kaiser, M.S. and Mahmud, M. (2023). An attention-based hybrid architecture with explainability for depressive social media text detection in Bangla, Expert Systems with Applications 213: 119007, DOI: 10.1016/j.eswa.2022.119007.]Search in Google Scholar
[Gopalakrishnan, R. (2023). Attribute selection hybrid network model for risk factors analysis of postpartum depression using social media, Brain Informatics 10(1): 28, DOI: 10.1186/s40708-023-00206-7.]Search in Google Scholar
[Gupta, G.K. and Sharma, D.K. (2023). Depression detection using semantic representation-based semi-supervised deep learning, International Journal of Data Analysis Techniques and Strategies 15(3): 217–237.]Search in Google Scholar
[Gupta, V. (2021). An emotion care model using multimodal textual analysis on COVID-19, Chaos Solitons Fractals 144: 110708, DOI: 10.1016/j.chaos.2021.110708.]Search in Google Scholar
[Kachuee, M., Fazeli, S. and Sarrafzadeh, M. (2018). ECG heartbeat classification: A deep transferable representation, arXiv: 1805.00794.]Search in Google Scholar
[Lilhore, U.K., Dalal, S., Varshney, N., Sharma, Y.K., Rao, K.B.V.B., Rao, V.V.R.M., Alroobaea R., Simaiya, S., Margala, M. and Chakrabarti, P. (2024). Prevalence and risk factors analysis of postpartum depression at early stage using hybrid deep learning model, Scientific Reports 14: 4533, DOI: 10.1038/s41598-024-54927-8.]Search in Google Scholar
[Nadeem, A., Naveed, M., Islam Satti, M., Afzal, H., Ahmad, T. and Kim, K.-I. (2022). Depression detection based on hybrid deep learning SSCL framework using self-attention mechanism: An application to social networking data, Sensors 22(24): 9775, DOI: 10.3390/s22249775.]Search in Google Scholar
[Nazir, A., Rao, Y., Wu, L. and Sun, L. (2022). Issues and challenges of aspect-based sentiment analysis: A comprehensive survey, IEEE Transactions on Affective Computing 13(2): 845–863.]Search in Google Scholar
[Nti, I.K. and Owusu-Boadu, B. (2022). A hybrid boosting ensemble model for predicting maternal mortality and sustaining reproductive health, Smart Health 26: 100325, DOI: 10.1016/j.smhl.2022.100325.]Search in Google Scholar
[Oueslati, O., Cambria, E., HajHmida, M.B. and Ounelli, H. (2020). A review of sentiment analysis research in Arabic language, arXiv: 2009.01360.]Search in Google Scholar
[Pooja and Bhalla, R. (2022). A review paper on the role of sentiment analysis in quality education, SN Computer Science 3(6): 469.]Search in Google Scholar
[Rahman, A. and Alam, M.G.R. (2023). Explainable AI based maternal health risk prediction using machine learning and deep learning, 2023 IEEE World AI IoT Congress (AIIoT), Seattle, USA, pp. 0013–0018, DOI: 10.1109/AIIoT58121.2023.10174540.]Search in Google Scholar
[Shoumy, N.J., Ang, L.-M., Seng, K.P., Rahaman, D.M.M. and Zia, T. (2020). Multimodal big data affective analytics: A comprehensive survey using text, audio, visual and physiological signals, Journal of Network and Computer Applications 149: 102447, DOI: 10.1016/j.jnca.2019.102447.]Search in Google Scholar
[Teisseyre, P. (2022). Joint feature selection and classification for positive unlabelled multi-label data using weighted penalized empirical risk minimization, International Journal of Applied Mathematics and Computer Science 32(2): 311–322, DOI: 10.34768/amcs-2022-0023.]Search in Google Scholar
[Titla-Tlatelpa, J. de J., Ortega-Mendoza, R.M., Montes-y-G´omez, M. and Villaseñor-Pineda, L. (2021). A profile-based sentiment-aware approach for depression detection in social media, EPJ Data Science 10, Article no. 54, DOI: 10.1140/epjds/s13688-021-00309-3.]Search in Google Scholar
[Togunwa, T.O., Babatunde, A.O. and Abdullah, K.U. (2023). Deep hybrid model for maternal health risk classification in pregnancy: Synergy of ANN and random forest, Frontiers in Artificial Intelligence 6: 1213436, DOI: 10.3389/frai.2023.1213436.]Search in Google Scholar
[Wang, S., Chen, J. and Lu, Z. (2021). Multimodal sentiment analysis using transformer-based approaches, IEEE Transactions on Affective Computing 12(1): 82–93.]Search in Google Scholar
[Xue, Y., Wang, Y. and Yu, M. (2020). Cross-modal sentiment analysis with sentiment-enhanced multimodal fusion, Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 5677–5687, (online).]Search in Google Scholar
[Zhang, J., Yin, Z., Chen, P. and Nichele, S. (2020). Emotion recognition using multimodal data and machine learning techniques: A tutorial and review, Information Fusion 59: 103–126, DOI: 10.1016/j.inffus.2020.01.011.]Search in Google Scholar
