A deep learning based hybrid model for maternal health risk detection and multifaceted emotion analysis in social networks

Geethanjali, R.  ; Valarmathi, A.

International Journal of Applied Mathematics and Computer Science, Tome 34 (2024) no. 4, pp. 565-577.

Résumé

In the field of public health, accurately identifying maternal health risks through social network data is both vital and challenging due to the complexities of multimodal sentiment analysis. Our study addresses this challenge by introducing the maternal health risk factor detection using deep learning approach (MHRFD-DLA), a novel framework that integrates convolutional neural networks, long short-term memory networks, and attention mechanisms. This approach enhances sentiment analysis and risk detection in maternal health, with the focus on critical areas such as prenatal care, mental health, and nutrition. MHRFD-DLA utilizes multimodal data, including text and electrocardiogram (ECG) signals, offering a comprehensive assessment of maternal health risks. Our model outperforms existing multimodal sentiment analysis models, achieving an accuracy of 98.4

Détail

Keywords: multifaceted emotion analysis, social network, maternal health, risk factor detection, deep learning, hybrid approach
Mots-clés : analiza emocji, sieć społecznościowa, zdrowie matki, wykrywanie czynników ryzyka, uczenie głębokie, podejście hybrydowe

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