During childhood, parents represent the most commonly used source of their child’s temperament information and, typically, do so by responding to questionnaires. Despite their wide-ranging applications, interviews present notorious data reduction challenges, as quantification of narratives has proven to be a labor-intensive process. However, for the purposes of this study, the labor-intensive nature may have conferred distinct advantages. The present study represents a demonstration project aimed at leveraging emerging technologies for this purpose. Specifically, we used Python natural language processing capabilities to analyze semistructured temperament interviews conducted with U.S. and German mothers of toddlers, expecting to identify differences between these two samples in the frequency of words used to describe individual differences, along with some similarities. Two different word lists were used: (a) a set of German personality words and (b) temperament-related words extracted from the Early Childhood Behavior Questionnaire (ECBQ). Analyses using the German trait word demonstrated that mothers from Germany described their toddlers as significantly more “cheerful” and “careful” compared with U.S. caregivers. According to U.S. mothers, their children were more “independent,” “emotional,” and “timid.” For the ECBQ analysis, German mothers described their children as “calm” and “careful” more often than U.S. mothers. U.S. mothers, however, referred to their children as “upset,” “happy,” and “frustrated” more frequently than German caregivers. The Python code developed herein illustrates this software as a viable research tool for cross-cultural investigations.
Towards Accurate Deceptive Opinions Detection Based on Word Order-Preserving CNN
