A Sentimental Analysis Approach Using Stacked Gaussian Deep Learning for Understanding the Connection between Family Dynamics and Emotional Shifts in Deep Sea

Authors

  • Yuan Zhang Krirk Univercity, Bangkok 10220, Thailand

Keywords:

Stacked gaussian deep learning, Film analysis, reactions dynamics, sentimental analysis, deep family makeup

Abstract

In recent years, advancements in deep learning and natural language processing techniques have opened up new avenues for analysing and understanding human emotions and social dynamics. One such approach is the use of stacked deep learning models, which leverage the power of multiple layers of neural networks to capture complex relationships and patterns in data. Family dynamics play a crucial role in shaping individuals' reactions well-being and overall health. This paper examines the relationship between the family and reactions changes in family Makeup. The model is examined with sentimental analysis based architectural model Stacked Gaussian Deep Learning (SgDL). The proposed SgDL model uses the probability distribution model for the estimation of the relationship between family and the emotions of people. The constructed SgDL model uses the Gaussian Distribution based stacked architecture model for the sentimental analysis to estimate the relationship between family and emotions of people. Simulation analysis stated that the proposed SgDL model achieves significant performance towards the computation of the relationship between family and relationship for the reaction’s changes. The performance of the SgDL model achieves a higher classification accuracy of 97.35% which is ~6% - 7% higher than the conventional CNN and LSTM model.

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Published

30.11.2023

How to Cite

Zhang , Y. . (2023). A Sentimental Analysis Approach Using Stacked Gaussian Deep Learning for Understanding the Connection between Family Dynamics and Emotional Shifts in Deep Sea. International Journal of Intelligent Systems and Applications in Engineering, 12(6s), 481–494. Retrieved from https://www.ijisae.org/index.php/IJISAE/article/view/3990

Issue

Vol. 12 No. 6s (2024)

Section

Research Article

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