A Text-based Intelligently driven Emotion Recognition Framework

Introduction: Emotion recognition from text has gained considerable interest due to its applications in human-computer interaction, emotion analysis, and psychiatric research. Traditional methods have struggled with emotional ambiguity, cultural nuances, and the dynamic nature of language, which affect the reliability of emotion recognition. This paper presents a novel emotion recognition framework named Artificial Gorilla Troops driven Modified Gaussian Support Vector Machine (AGT-MGSVM).Methods: We gathered a publicly available ISEAR dataset containing various textual emotional expressions and applied natural language processing (NLP) techniques for text pre-processing. The suggested AGT-MGSVM approach combines the resilience of the Gaussian assist vector gadget (GSVM) with the ability of AGT, a bio-stimulated optimization method. AGT improves the MGSVM with the aid of dynamically regulating its parameters based on the evolutionary conduct of gorilla troops, optimizing the version to enhance emotion popularity. Results: We examine the performance of the proposed technique against traditional emotion reputation methods using standard metrics inclusive of recall (89.2%), precision (89.5%), F1-score (89.4%), and accuracy (89.9%). Conclusion: The counseled AGT-MGSVM method is a promising improvement in intelligence-driven emotion reputation from the text.