The field of sign language recognition and emotion analysis is rapidly evolving, with a focus on developing more accurate and efficient models for recognizing sign language gestures and capturing emotional expressiveness. Recent studies have explored the use of lightweight transformer models, such as TSLFormer, which achieves competitive performance with minimal computational cost. Other research has investigated the importance of capturing emotional nuance in sign language, highlighting the role of both manual and non-manual elements in emotional expression. The development of multilingual frameworks, like S-DAT, has also enabled the assessment of divergent thinking across diverse languages and cultures. Noteworthy papers include: TSLFormer, which presents a lightweight transformer model for Turkish Sign Language recognition. Perspectives on Capturing Emotional Expressiveness in Sign Language, which explores the emotional dimensions of sign language communication through semi-structured interviews with sign language users.