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

TL;DR

This paper presents a hybrid method combining DistilBERT and emoji sentiment analysis to effectively track emotional dynamics in chat conversations, outperforming traditional machine learning models in recognizing emotions in real-time social media data.

Contribution

It introduces a novel hybrid approach that integrates text-based emotion detection with emoji sentiment analysis for improved emotional tracking in chat conversations.

Findings

DistilBERT outperforms SVM, Random Forest, and AdaBoost in emotion recognition.

Combining text and emoji analysis enhances emotional understanding in chats.

The approach is effective in real-time emotional shift detection.

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.