Publication

Abstract : In the digital age, recognizing emotions from text has become increasingly important for understanding online interactions and supporting applications in mental health, business, and interaction with human. This study addresses fine-grained emotion recognition in Reddit comments using the GoEmotions dataset, which includes more than 58,000 samples spanning 27 emotions and a neutral class. We look into the application of modern natural language processing methods by fusing deep neural architectures like Convolutional Neural Networks, Bidirectional Long Short-Term Memory, and Cross-Lingual Models (XLM) with pre-trained contextual embeddings like BERT, RoBERTa, Dis-tilBERT, and ELECTRA. Multiple dataset configurations were employed, ranging from the full 28-class setting to reduced taxonomies of 27, 7, and 6 emotions, allowing us to evaluate performance across different levels of granularity. Comparative experiments highlight the strengths and weaknesses of CNN, RNN, and transformer-based approaches in multi-class emotion classification. Results indicate that transformer-CNN hybrids consistently outperform other architectures, with the BERT+CNN model achieving the highest weighted F1-score of 81% on the 6-class dataset. These findings emphasize the synergy between contextual embeddings and localized feature extraction, demonstrating their Effectiveness in fine-grained emotion prediction and their potential to enhance real-time emotion-aware applications.