Multimodal Deep Learning Models for Unstructured Data Integration in Enterprise Analytics
Keywords:
Multi-Modal, Unstructured Data, AI-Based, Component Analysis, Divide-And Conquer, Sentiment Modeling, Data Aggregation Module, Novel Feature, Proposed Techniques.Abstract
The necessity for sophisticated processing systems that can effectively extract valuable insightshas increased due to the growth of multi-modal unstructured data. Despite the potential, currentstudies show significant limitations
References
P. Kherwa, A. Sachdeva, D. Mahajan, N. Pande, and P. K. Singh, ‘‘An approach towards comprehensive sentimental data analysis and opinion mining,’’ in Proc. IEEE Int. Adv. Comput. Conf. (IACC), Feb. 2014, pp. 606–612.
H. Ha, W. Hwang, S. Bae, H. Choi, H. Han, G. N. Kim, and K. Lee, ‘‘CosMovis: Semantic network visualization by using sentiment words of movie review data,’’ in Proc. 19th Int. Conf. Inf. Vis., vol. 19, Jul. 2015, pp. 436–443.
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Published
2025-08-26
How to Cite
Suresh Sankara Palli. (2025). Multimodal Deep Learning Models for Unstructured Data Integration in Enterprise Analytics. Journal of Computational Analysis and Applications (JoCAAA), 34(8), 125–140. Retrieved from https://eudoxuspress.com/index.php/pub/article/view/3495
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