Text mining and sentiment analysis: A new lens to explore the emotion dynamics of mother‐child interactions

Abstract

Emotions are highly dynamic and social in nature. Traditional approaches to studying emotion expression face obstacles such as substantial time investments, susceptibility to human biases, and limited capacity to capture nuanced emotional patterns. To address these challenges, this research leveraged text mining and sentiment analysis to explore the dynamic patterns of emotion expression within the context of mother‐child interactions. We analyzed 8,841 conversation transcripts involving 1,462 mother‐child dyads, sourced from the Child Language Data Exchange System. Polarity scores were calculated and analyzed to uncover the temporal patterns of mother and child emotional sentiment. Our findings revealed that mothers tended to exhibit heightened levels of positive emotion at the beginning and conclusion of conversations, whereas children displayed a more linear positive trend. Using model‐based cluster analysis, we identified two distinct clusters of mothers characterized by varying degrees of emotion expression variation and two clusters of children showing different rates of elevation in positive emotion. At the dyadic level, the differences between mother and child polarity scores varied as a function of time, with an increase of difference from the beginning to the 20th percentile point, a decrease until the 90th percentile, and then an increase again towards the end of the conversation. This study demonstrates the utility of text mining and sentiment analysis in developmental studies, particularly in the context of parent‐child interactions. The findings hold informative implications for interventions that focus on fostering healthy parent‐child relationships.