The objective of this paper is to predict (A) whether a sentence in a written text expresses an emotion, (B) the mode(s) in which it is expressed, (C) whether it is basic or complex, and (D) its emotional category. One of our major contributions, through a dataset and a model, is to integrate the fact that an emotion can be expressed in different modes: from a direct mode, essentially lexicalized, to a more indirect mode, where emotions will only be suggested, a mode that NLP approaches generally don't take into account. Another originality is that the scope is on written texts, as opposed usual work focusing on conversational (often multi-modal) data. In this context, modes of expression are seen as a factor towards the automatic analysis of complexity in texts. Experiments on French texts show acceptable results compared to the human annotators' agreement, and outperforming results compared to using a large language model with in-context learning (i.e. no fine-tuning).

翻译：本文的目标是预测书面文本中的句子是否表达情感（A）、其表达模式（B）、该情感是基本还是复杂（C）以及其情感类别（D）。我们的主要贡献之一是通过一个数据集和一个模型，整合了情感可以以不同模式表达的事实：从本质上词汇化的直接模式，到情感仅被暗示的更间接的模式，后者通常是自然语言处理方法所未考虑的。另一项创新在于研究范围聚焦于书面文本，这与通常关注对话式（常为多模态）数据的研究不同。在此背景下，表达模式被视为实现文本复杂度自动分析的一个因素。在法语文本上的实验结果表明，与人工标注者的一致性相比取得了可接受的结果，并且相较于使用大型语言模型进行上下文学习（即未进行微调）的方法表现更优。