Tracking Emotional Dynamics in Chat Conversations: A Hybrid Approach using DistilBERT and Emoji Sentiment Analysis

TL;DR

This paper explores a hybrid approach to tracking emotional dynamics in chat conversations by combining DistilBERT-based text emotion detection and emoji sentiment analysis, and shows that integrating text and emoji analysis is an effective way of tracking chat emotion.

Abstract

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.

Figures (15)

• Figure 1: Aggregated emotion frequencies across the collected tweets.
• Figure 2: Word cloud generated from the Twitter dataset
• Figure 3: Bar chart illustrating the frequency of each emotion in the dataset
• Figure 4: Word cloud representing the linguistic patterns associated with various emotions in the Kaggle dataset
• Figure 5: A bar chart representing the sentiment scores of various emojis, based on the standardization by esr