Using sentiment analysis to detect affect in children’s and adolescents’ poetry

Sentiment analysis is a computational method that automatically analyzes the valence of massive quantities of text. Basic sentiment analysis involves extracting and counting emotionally-laden keywords from passages of text (e.g., hate, love, happy, sad). This study describes using sentiment analysis to explore changes in emotion expression in a developmental context. A sample of n = 8,688 poems published online by children and adolescents from Grade 4 to Grade 12 was analyzed. Sentiment analysis coded words as positive or negative and these were averaged within each poem to obtain its relative percentage of positive and negative sentiment. Polynomial regressions explored linear and nonlinear trends in sentiment scores by grade. Among the results, negative sentiment demonstrated an upward curvilinear trend, increasing sharply from Grade 6 to Grade 11 and then decreasing afterward. Positive sentiment demonstrated a sinusoidal pattern throughout development. Overall, these findings are consistent with previous research on the progressions of emotion expression in childhood and adolescence. Despite some limitations, sentiment analysis presents an opportunity for researchers in developmental psychology to explore basic questions in emotional development using large quantities of data