EMOVAL : évaluation automatique de la valence et de l’activation émotionnelles des textes à l’aide d’une méta-norme de 5656 mots-racines

Abstract

EMOVAL: automatic evaluation the emotional valence and arousal of texts using a 5656 root-words metanorm. EMOVAL is an emotional valence and arousal analysis model of texts. EMOVAL draws from linguistic tradition the hypothesis that every word has a denotative aspect (“meaning”) and a connotative aspect (“affective halo”). It uses a meta-analysis of seven French norms and one English norm with the objective to characterize the emotional valence of texts, paragraphs, or sentences in a pleasant or unpleasant way. The meta-analysis indicates that the seven French norms data are highly correlated in between (0.82 to 0.99), and highly correlated with the Affectiv Norm for English Words ( Bradley et Lang, 1999) (0.81 to 0.97). Arousal values taken from Affective Norm for English Words (ANEW) ( Bradley et Lang, 1999) and the Leleu (1987) norm are significately correlated (0.55). The metanorm has 5656 words (nouns, verbs, adjectives, adverbs) characterized in valence (−1 to +1), and 3265 words characterized in arousal. These items are used by EMOVAL for valence judgments of texts. Two types of texts are proposed: the evaluation of the whole (702) or of extracts (110) of a corpus of sentences judged in a seven-point scale (−3 very unpleasant to +3 very pleasant) ( Bestgen et al., 2004), and of 12 texts positively valenced (happiness and good surprise) and negatively valenced (fear, anger, disgust, sadness, and bad surprise). These two types of tests confirm the psychological pertinence of EMOVAL. Limits regarding the arousal dimension are discussed. The metanorm presented in this article can be obtained from the authors.