Text-Based Emotion Analysis: Approaches and Evaluations

Emotions have an effect on human conduct, affecting interactions, choices, and ordinary functioning. Emotion detection can help businesses personalize services and help in diagnosing intellectual fitness problems. This challenge makes utilisation the ISEAR dataset, which incorporates seven emotion classes, to detect emotions from textual information. We integrate Convolutional Neural Networks (CNN), Bidirectional Gated Recurrent Units (BiGRU), and Support Vector Machines (SVM) to deal with the complexity of emotion expression in text. Our version achieves an 86% accuracy rate. The outcomes highlight the model’s effectiveness and its capacity programs in improving purchaser interactions and intellectual health diagnostics. This work advances natural language processing techniques for real-world applications.