Predicting Emotion Intensity in Text Using Transformer-Based Models

AbstractEmotion intensity prediction in text enhances conversational AI by enabling a deeper understanding of nuanced human emotions, a crucial yet underexplored aspect of natural language processing (NLP). This study employs Transformer-based models to classify emotion intensity levels (0–3) for five emotions: anger, fear, joy, sadness, and surprise. The dataset, sourced from the SemEval shared task, was preprocessed to address class imbalance, and model training was performed using fine-tuned *bert-base-uncased*. Evaluation metrics showed that *sadness* achieved the highest accuracy (0.8017) and F1-macro (0.5916), while *fear* had the lowest accuracy (0.5690) despite a competitive F1-macro (0.5207). The results demonstrate the potential of Transformer-based models in emotion intensity prediction while highlighting the need for further improvements in class balancing and contextual representation.