Comparing Machine Learning-Centered Approaches for Forecasting Language Patterns During Frustration in Early Childhood
Year: 2021
Type: preprint
Abstract: When faced with self-regulation challenges, children have been known the use their language to inhibit their emotions and behaviors. Yet, to date, there has been a critical lack of evidence regarding what patterns in their speech children use during these moments of frustration. In this paper, eXtreme Gradient Boosting, Random Forest, Long Short-Term Memory Recurrent Neural Networks, and Elastic Net Regression, have all been used to forecast these language patterns in children. Based on the results of a comparative analysis between these methods, the study reveals that when dealing with high-dimensional and dense data, with very irregular and abnormal distributions, as is the case with self-regulation patterns in children, decision tree-based algorithms are able to outperform traditional regression and neural network methods in their shortcomings.
Source: arXiv (Cornell University)
Authors: Bhakta, Arnav, Kim, Yeunjoo, Cole, Pamela
Institution: Pennsylvania State University
Language: English
Topic: Emotion and Mood Recognition
Subfield: Experimental and Cognitive Psychology
Field: Psychology
Domain: Social Sciences
Open Access status: green
OpenAlex ID: W3211199135