Unveiling the tapestry of mother–child interactions through text mining and sentiment analysis

Abstract

The interaction between a mother and child stands as one of the most profound and intricate human connections, weaving a rich tapestry of behavioral and emotional bonds during the formative years. Although mother–child interactions have received substantial attention in the developmental science literature, few studies have tapped into the extensive corpus of speech data available to uncover the nuances of these interactions across developmental stages. This study applied text mining and sentiment analysis on narratives extracted from mother–child conversations to identify the developmental trend of mother–child interactions from early to middle childhood. The results, based on three key areas of dyadic interactions, demonstrated a shift toward more balanced turn-taking dynamics and linguistic congruence as children age. Also, there was a significant interdependence of mother and child expressed emotions across time. Further investigation of the dyadic emotionality revealed a nonlinear effect of mother-expressed emotion on child-expressed emotion: mother-expressed negative emotions followed a cubic-like pattern, while positive emotions followed a mild quadratic trend. Taken together, the findings of this study present a picture of progressive augmentation of mother–child synchrony over time.