A Comprehensive Review on Emotion Detection in Code-Mixed Social Media Posts
Nikhil Tripathi, Mukhtiar Singh, Nikita Singh Yadav · 2024
Emotion detection in social media posts has become a crucial area of research, especially with the growing presence of code-mixed content—posts that blend multiple languages. This review paper offers a thorough examination of current approaches and difficulties related to emotion recognition in social media posts with mixed codes. We explore a range of methods, from state-of-the-art deep learning architectures to conventional machine learning techniques, and show how well they handle the linguistic intricacies of code-mixed text. The study also addresses how natural language processing (NLP) technologies handle multilingual data and how different language pairs affect the precision of emotion recognition. The review also emphasizes the significance of accurate emotion detection in applications like mental health monitoring and social media analytics, pointing out the necessity for culturally adaptive models. By identifying research gaps and proposing future directions, this review aims to guide researchers and practitioners in advancing emotion detection systems for the increasingly diverse and multilingual social media landscape.