Deep Fusion for Deeper Feelings: Uniting Visual and Textual Cues in Emotion Detection

Emotion detection has enormous applications in mental health care, crisis support, and customer engagement, where it can make a big difference. This framework exploits advanced text and image analysis by including both textual and visual inputs. It integrates XLM-RoBERTa for multilingual text processing and pre-trained CNN (ResNet) for visual emotion detection, and a fusion mechanism is used to combine information from both sources to further allow for more accurate, subtle identification of emotional states. The framework was tested against the GoEmotions dataset for text-based emotion detection and FER-2013 for facial expression recognition with promising results in terms of both accuracy and robustness. It can contribute to the framework using both linguistic and visual cues towards more accurate emotion detection, which can then be pivotal in health care to detect emotional distress at its onset, thereby opening windows for timely interventions. Besides that, it would help monitor social media based on the understanding of public sentiment better. This approach advances the development of emotionally intelligent AI systems that can empathetically respond to a wide range of human emotions, with applications in creating inclusive and impactful affective computing solutions. This work is a step toward more responsive and culturally adaptable AI systems.