STUDY AND COMPARATIVE ANALYSIS OF DEEP LEARNING TECHNIQUES FOR EMOTION DETECTION IN TEXTUAL DATA

Abstract

Detecting emotions from textual data is increasingly significant in various domains such as mental health support, customer experience enhancement, and social media analysis. As digital communication grows, understanding the emotional tone of written content has become essential for building responsive and human-centric applications. This study explores recent developments in deep learning techniques that aim to improve the accuracy and efficiency of emotion classification in text. In-depth analysis is conducted on a variety of deep learning architectures from basic ones like Bi-LSTM, Bi-GRU, ANN, CNN to complex transformer-based models like BERT, RoBERTa, and GPT. The models are evaluated on three widely used datasets—ISEAR, GoEmotion, and MELD— chosen for their diversity in the use of language as well as emotion categories. For the sake of comparison, the three datasets went through the same preprocessing including text cleaning, normalization, tokenization, and encoding. The aim of this research work is to analyze and compare the performance of the models under uniform training conditions and metric parameters like accuracy and F1-score. Experimentation outcomes show that transformer models provide enhanced performance by efficiently comprehending the contextually suitable sense of emotion in words. This comparative review not only unveils the potential of existing models but also reveals where more can be gained, the keys to even more sophisticated and sensitive emotion recognition systems.