Comparison Of Emotion Through Text Analysis Using Various Deep Learning Models

In this study, we investigate the efficacy of Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), Long Short-Term Memory networks (LSTM), and Bidirectional LSTMs (BiLSTM) for emotion classification tasks, which are crucial in natural language processing for applications ranging from sentiment analysis to mental health monitoring. After preprocessing textual data and encoding it into numerical representations suitable for deep learning models, we train multiple architectures with varied configurations and hyperparameters. Evaluation based on accuracy, precision, recall, and F1-score reveals distinct strengths and weaknesses of each model. CNNs excel in capturing local features, RNNs adeptly model sequential dependencies, LSTM networks overcome vanishing gradient issues for long-range dependencies, and BiLSTMs outperform by incorporating bidirectional information flow, enhancing contextual understanding. Our findings offer valuable insights into selecting appropriate deep learning models for emotion analysis, advancing the state-of-the-art in these fields, and guiding practitioners in their applications.