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

arXiv: https://arxiv.org/abs/2408.01838 · HTML: https://mlsanity.com/paper/2408.01838/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 39 references that are in the map.
- First posted on arXiv 3 Aug 2024.

## Builds on (most cited first, 10 of 10)

- [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter](https://mlsanity.com/paper/1910.01108/) (2019)
- [Emotion Recognition in Conversation: Research Challenges, Datasets, and Recent Advances](https://mlsanity.com/paper/1905.02947/) (2019)
- [EmotionLines: An Emotion Corpus of Multi-Party Conversations](https://mlsanity.com/paper/1802.08379/) (2018)
- [Sentiment of Emojis](https://mlsanity.com/paper/1509.07761/) (2015)
- [Color-Emotion Associations in Art: Fuzzy Approach](https://mlsanity.com/paper/2311.18518/) (2023)
- [Towards Building an Open-Domain Dialogue System Incorporated with Internet Memes](https://mlsanity.com/paper/2203.03835/) (2022)
- [Fuzzy Approach for Audio-Video Emotion Recognition in Computer Games for Children](https://mlsanity.com/paper/2309.00138/) (2023)
- [Group Movie Selection using Multi-channel Emotion Recognition](https://mlsanity.com/paper/2403.12087/) (2024)
- [Collective emotion dynamics in chats with agents, moderators and Bots](https://mlsanity.com/paper/1411.5392/) (2014)
- [Multi-channel Emotion Analysis for Consensus Reaching in Group Movie Recommendation Systems](https://mlsanity.com/paper/2404.13778/) (2024)

## Built on by (most cited first, 2 of 2)

- [Meme Similarity and Emotion Detection using Multimodal Analysis](https://mlsanity.com/paper/2503.17493/) (2025)
- [Emoji-Emotion Ranking System Using Twitter Data](https://mlsanity.com/paper/2610.04495/) (2026)

## Abstract (arXiv)

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.