Emotion Identification for French in Written Texts: Considering their Modes of Expression as a Step Towards Text Complexity Analysis

TL;DR

This paper introduces a novel approach to identify emotions, their modes of expression, and text complexity in written French texts, emphasizing indirect expressions often overlooked by NLP models.

Contribution

It presents a new dataset and model that account for different modes of emotion expression in written texts, advancing text complexity analysis.

Findings

Acceptable emotion prediction accuracy matching human agreement

Outperforms large language models without fine-tuning

Highlights importance of expression modes in emotion detection

Abstract

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).