  Computer-mediated communication has become more important than face-to-face
communication in many contexts. Tracking emotional dynamics in chat
conversations can enhance communication, improve services, and support
well-being in various contexts. This paper explores a hybrid approach to
tracking emotional dynamics in chat conversations by combining DistilBERT-based
text emotion detection and emoji sentiment analysis. A Twitter dataset was
analyzed using various machine learning algorithms, including SVM, Random
Forest, and AdaBoost. We contrasted their performance with DistilBERT. Results
reveal DistilBERT's superior performance in emotion recognition. Our approach
accounts for emotive expressions conveyed through emojis to better understand
participants' emotions during chats. We demonstrate how this approach can
effectively capture and analyze emotional shifts in real-time conversations.
Our findings show that integrating text and emoji analysis is an effective way
of tracking chat emotion, with possible applications in customer service, work
chats, and social media interactions.
