  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).
