Abstract
The present study develops key research for French word norms that combines the predominant theories of dimensional and discrete (or categorical) emotions. As a result, we provide the database FANCat, affective norms for a set of 1031 French words on ten discrete emotion categories: fear, anger, disgust, sadness, anxiety, awe, excitement, contentment, amusement, and serenity. FANCat complements a previous word set, FAN, which provides only the dimensional norms, valence, and arousal (Monnier & Syssau, 2014). Herein, we introduce five discrete positive emotions in efforts to differentiate positive emotions at higher resolution and specificity. Although ten emotional categories were considered in FANCat norms, results showed a high degree of inter-rater reliability and a good external validity. Then, distributional analyses of words into the ten emotion categories revealed that positive words evoked mainly the emotions awe, contentment, and amusement, and principally evoked either one positive emotion only (“pure” words) or two (mixed words). This study contributes to a deeper understanding of the relationship between language, and negative and positive emotions. It is also currently the only norms database in French that analyses ten discrete emotions as well as including valence and arousal. FANCat is available at https://www.researchgate.net/publication/338622765_FANCat_database.