Human sentiment analytics using multi model deep learning approach - Kumar Muthevi, Anil; Venkatesh, Maganti; Adke, Pallavi Gaurav; Gadhave, Rajashree Tukaram; Vanguri, G L Narasamba; Srinivasulu, Thiruveedula (IAES International Journal of Artificial Intelligence (IJ-AI), 2025)
For assessing human beings, the measurement of willpower and human emotions plays an important role because human beings are emotional creatures. Emotional analysis, also known as sentiment analysis, is the process of using natural language processing (NLP) and machine learning to determine the emotions expressed in text, speech, or other forms of communication. However, critical emotional analysis is limited to human interactions only. Human emotional artificial intelligence or Human sentimental analytics, a sub domain of NLP seeks to improve this understanding. The Present study develops a model using multi model deep learning approach which is capable of efficiently understanding human emotions and their intentions, closely mirroring human cognition. By extending emotional analysis beyond the traditional limits, this model will collect broad ranging data to uncover clear and hidden emotional details. The primary objective of this paper is to build highly effective model which provides in-depth insights into human emotions, leading to logical conclusions depending on all available factors and reasons. The necessary input data for the current study will be collected from audio-visual media covering a vast range of audio and visual samples.