# AtmosERC: Modeling Dialogue-Level Affective Atmosphere for Emotion Recognition in Conversation

arXiv: https://arxiv.org/abs/2607.26726 · HTML: https://mlsanity.com/paper/2607.26726/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 16 of its 36 references that are in the map.
- First posted on arXiv 29 Jul 2026.

## Builds on (most cited first, 16 of 16)

- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [DailyDialog: A Manually Labelled Multi-turn Dialogue Dataset](https://mlsanity.com/paper/1710.03957/) (2017)
- [MELD: A Multimodal Multi-Party Dataset for Emotion Recognition in Conversations](https://mlsanity.com/paper/1810.02508/) (2018)
- [LLMs Get Lost In Multi-Turn Conversation](https://mlsanity.com/paper/2505.06120/) (2025)
- [DialogueRNN: An Attentive RNN for Emotion Detection in Conversations](https://mlsanity.com/paper/1811.00405/) (2018)
- [COSMIC: COmmonSense knowledge for eMotion Identification in Conversations](https://mlsanity.com/paper/2010.02795/) (2020)
- [Emotion Recognition in Conversation: Research Challenges, Datasets, and Recent Advances](https://mlsanity.com/paper/1905.02947/) (2019)
- [Emotion Detection on TV Show Transcripts with Sequence-based Convolutional Neural Networks](https://mlsanity.com/paper/1708.04299/) (2017)
- [MMGCN: Multimodal Fusion via Deep Graph Convolution Network for Emotion Recognition in Conversation](https://mlsanity.com/paper/2107.06779/) (2021)
- [Knowledge-Enriched Transformer for Emotion Detection in Textual Conversations](https://mlsanity.com/paper/1909.10681/) (2019)
- [DialogXL: All-in-One XLNet for Multi-Party Conversation Emotion Recognition](https://mlsanity.com/paper/2012.08695/) (2020)
- [EmoBERTa: Speaker-Aware Emotion Recognition in Conversation with RoBERTa](https://mlsanity.com/paper/2108.12009/) (2021)
- [M3ED: Multi-modal Multi-scene Multi-label Emotional Dialogue Database](https://mlsanity.com/paper/2205.10237/) (2022)
- [Multimodal Large Language Models Meet Multimodal Emotion Recognition and Reasoning: A Survey](https://mlsanity.com/paper/2509.24322/) (2025)
- [LaERC-S: Improving LLM-based Emotion Recognition in Conversation with Speaker Characteristics](https://mlsanity.com/paper/2403.07260/) (2024)
- [ERNetCL: A novel emotion recognition network in textual conversation based on curriculum learning strategy](https://mlsanity.com/paper/2308.06450/) (2023)

## Abstract (arXiv)

Emotion Recognition in Conversation (ERC) plays a key role in developing emotionally responsive conversational systems, and contextual modeling is central to this task. Several studies have developed contextual models that capture dialogue history, speaker interactions, and emotion dynamics to support emotion prediction. However, the specific role of affective atmosphere, a latent, conversation-level emotional tendency shaped by conversational content and speaker interactions, remains insufficiently understood. This paper investigates what role atmosphere plays in conversational emotion understanding. We first propose AtmosERC, a graph-based atmosphere extractor that utilizes conversational context and speaker interactions to produce a compact representation of affective atmosphere. We then evaluate two downstream uses of the extracted atmosphere: initializing lightweight ERC model and providing emotion cues for LLM-based ERC. Experiments on three widely used ERC benchmarks show that the estimated atmosphere improves emotion prediction across both applications and supports more reliable predictions when local emotions deviate from the overall affective tendency.