Assessing emotion in L2 writing: Validating Watson NLU with emotional vocabulary training

Abstract

Affective word values have been widely studied across languages, often focusing on isolated words due to the difficulty of assessing emotionality in texts. This study examines whether written emotional content can be reliably captured using a specific software tool (Watson Natural Language Understanding). Thirty-three Spanish undergraduates wrote 150-word autobiographical texts in their L2 (English) before and after a training with emotional vocabulary. Normative valence ratings of content words obtained in the pre- and post-training phases were compared with sentiment scores generated by Watson NLU. Strong positive correlations were found between sentiment and normative valence scores in both phases, with stronger relations at post-training. Regression analyses confirmed that sentiment scores significantly predicted normative valence. Importantly, while normative valence did not differ between phases, sentiment scores increased after training. These results suggest that Watson NLU is a valid and sensitive tool for assessing emotionality in written language and its modulation through training.