Text Mining

This study utilized text mining and sentiment analysis on mother-child conversations to examine the developmental trends in their interactions from early to middle childhood. The findings revealed a shift towards more balanced interactions as children age, with increased turn-taking dynamics and linguistic congruence, as well as a complex interdependence of expressed emotions between mothers and children over time.

This study employed text mining and sentiment analysis on over 8,800 mother-child conversation transcripts to examine dynamic patterns of emotion expression, revealing distinct temporal trends in emotional sentiment for both mothers and children. The research identified clusters of mothers and children with varying emotion expression patterns, and demonstrated how mother-child polarity score differences fluctuated throughout conversations, showcasing the potential of these analytical methods for understanding parent-child interactions and informing relationship-focused interventions.