  Explainable Multimodal Emotion Recognition (EMER) is an emerging task that
aims to achieve reliable and accurate emotion recognition. However, due to the
high annotation cost, the existing dataset (denoted as EMER-Fine) is small,
making it difficult to perform supervised training. To reduce the annotation
cost and expand the dataset size, this paper reviews the previous dataset
construction process. Then, we simplify the annotation pipeline, avoid manual
checks, and replace the closed-source models with open-source models. Finally,
we build \textbf{EMER-Coarse}, a coarsely-labeled dataset containing
large-scale samples. Besides the dataset, we propose a two-stage training
framework \textbf{AffectGPT}. The first stage exploits EMER-Coarse to learn a
coarse mapping between multimodal inputs and emotion-related descriptions; the
second stage uses EMER-Fine to better align with manually-checked results.
Experimental results demonstrate the effectiveness of our proposed method on
the challenging EMER task. To facilitate further research, we will make the
code and dataset available at: https://github.com/zeroQiaoba/AffectGPT.
