L'analyse automatisée du ton médiatique : construction et utilisation de la version française du<i>Lexicoder Sentiment Dictionary</i>
Bibliographic record
Abstract
Résumé Cet article introduit un nouveau dictionnaire permettant l'analyse automatisée du ton des médias francophones, que nous avons appeléLexicoder Sentiment Dictionnaire Français(LSDFr) en référence au lexique anglophone de Young et Soroka (2012),Lexicoder Sentiment Dictionary(LSD) à partir duquel leLSDFra été construit. Une fois construit, nous comparons leLSDFrau seul autre dictionnaire francophone existant de ce genre,Linguistic Inquiry and Word Count(LIWC). Nous testons ensuite la validité interne duLSDFren le comparant avec un corpus de textes codés manuellement. Nous testons enfin la validité externe duLSDFren mesurant jusqu'où le ton médiatique, calculé à l'aide de notre dictionnaire, prédit les intentions de vote des Québécois lors des quatre dernières campagnes électorales. En développant cet outil, notre objectif est de permettre à d'autres chercheurs d'effectuer des analyses médiatiques dans un corpus de textes comparables en français.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".