Enhancing Emotion Detection With Bidirectional GRU Networks: A Deep Learning Approach Using Dropout and Batch Normalization

Social media platforms, such as Twitter, produce significant quantities of textual data, providing important opportunities for the analysis of emotions. The identification of emotions within text is essential for applications including mental health monitoring, marketing strategies, and sentimentdriven recommendation systems. Traditional methods frequently encounter challenges associated with linguistic complexities and the ability to generalize across varied datasets. This paper proposes a novel method using Bidirectional Gated Recurrent Unit (BiGRU) networks with some regularization techniques such as dropout and batch normalization. The bidirectional architecture allows both forward and backward contextual information to be learned, thus it can express subtle emotional features effectively. Using a dataset of 89,832 labeled tweets from six different emotional classes—sadness, joy, love, anger, fear, and surprise—the model achieved an accuracy of 93.83%, precision of 93.83%, recall of 93.83%, and an F1-score of 93.83%. This demonstrates that integrating advanced neural architectures along with successful regularization techniques significantly improves the stability and effectiveness of emotion detection models.