Closed- and open-vocabulary approaches to text analysis: A review, quantitative comparison, and recommendations.

Technology now makes it possible to understand efficiently and at large scale how people use language to reveal their everyday thoughts, behaviors, and emotions. Written text has been analyzed through both theory-based, closed-vocabulary methods from the social sciences as well as data-driven, open-vocabulary methods from computer science, but these approaches have not been comprehensively compared. To provide guidance on best practices for automatically analyzing written text, this narrative review and quantitative synthesis compares five predominant closed- and open-vocabulary methods: Linguistic Inquiry and Word Count (LIWC), the General Inquirer, DICTION, Latent Dirichlet Allocation, and Differential Language Analysis.

PerCEIVED: Perceptions of children's emotions in videos, evolving and dynamic task.

Researchers have been studying emotion recognition skill for over 100 years (Feleky, 1914), yet technological advances continue to allow for the creation of better measures. Interest in consistent inaccuracies (sometimes described as bias) has also emerged recently. To support research in both emotion recognition skill and bias, we first describe all extant measures of emotion recognition with child actors that we have found, evaluating strengths and constraints of these measures.

Teachers' Beliefs About Children's Anger and Skill in Recognizing Children's Anger Expressions.

Everyday beliefs often organize and guide motivations, goals, and behaviors, and, as such, may also differentially motivate individuals to value and attend to emotion-related cues of others. In this way, the beliefs that individuals hold may affect the socioemotional skills that they develop. To test the role of emotion-related beliefs specific to anger, we examined an educational context in which beliefs could vary and have implications for individuals' skill.