Measuring teacher emotional language valence via sentiment analysis: Correlations with classroom practices

This study explores the use of sentiment analysis (i.e., a natural language processing [NLP] technique) to assess teacher emotional valence in speech (i.e., whether language is positive, negative, or neutral) during classroom interactions. We analyzed the emotional valence of 131 teachers across eight rural elementary schools and examined the correlations with observed instructional and behavioral management practices (i.e., general praise, behavior specific praise [BSP], behavioral correction, harsh correction, opportunities to respond [OTRs]) across two-time windows: a full school day and 15-min of teacher-led large-group instruction. Sentiment scores were derived using Linguistic Inquiry Word Count (LIWC-22) software, capturing both tone and emotionality metrics. Findings revealed weak to moderate positive correlations between emotional valence and praise, particularly general and combined praises. Emotionality showed stronger association with praise to correction ratios and was more sensitive to short-term emotional expression. No significant correlations were found with harsh corrections or OTRs. Additionally, moderate temporal stability was observed between full-day and 15-min emotional valence scores. Initial findings identified emotionality-capturing explicit emotional language-as a promising complement to traditional observation methods. We discuss findings and implications for integrating sentiment metrics into technological tools to support teacher development and classroom climate monitoring. Future research is needed to expand and validate preliminary findings across diverse educational contexts and explore whether emotional valence moderates or mediates the effectiveness of instructional practices.