We will present the results of three experiments on subjectivity detection in French press articles. Our research lies at the crossroads of journalism studies and linguistics and aims to uncover the mechanisms of objective writing in journalistic discourse. First, we evaluated a range of linguistic features for a text classification task of news articles and opinion pieces. Then, we fine-tuned a transformer model (CamemBERT) on the same task and compared it with the feature-based model in terms of accuracy, computational cost and explainability. We used model explanation methods to extract linguistic patterns from the transformer model in order to build a more accurate and more transparent hybrid classification model. Finally, we conducted an annotation experiment involving students in journalistic writing, asking them to highlight “subjective elements” in 150 Belgian press articles. This allowed us to compare human-based and machine-derived insights on subjectivity. This project also includes the analysis of Quebec French media articles, aiming to compare the linguistic objectivity of European and North American French journalism.

Enregistrement : https://drive.google.com/file/d/1btdXDHpa8U2iHG3OyVmh_Tu_D6GCXfFV/view?usp=drive_link