A COMPREHENSIVE STUDY OF EMOTION ANALYSIS IN ARTIFICIAL INTELLIGENCE: TECHNIQUES AND MODELS [Manuscript ID 2024/IJORAR/05/0115]

ABSTRACT: Emotion analysis uses AI and machine learning technologies to understand human feelings. Textual, visual, and acoustic analysis are the three main model types generally included in this area. At the heart of these models are neural networks, particularly complication neural networks (CNNs) and intermittent neural networks (RNNs), which are excellent at evoking emotions. This technology, sometimes referred to as affective computing or emotion AI, uses artificial intelligence to decode and learn from deadly emotional signals. Text, audio, and video data are each anatomized by emotion AI, enabling precise decoding of the emotional state. For example, to improve their appreciation of human emotions, many chatbots are currently integrating emotion recognition technologies such as speech analysis and facial recognition. To more directly detect emotional states, these systems examine physiological indicators, facial expressions, and oral intonations. This article introduces in detail the styles and models used to improve emotional analysis.