Emotional Cascade Model and Deep Learning

Abstract

Emotional Cascade Model proposes that the emotional and behavioral dysregulation of individuals with Borderline Personality Disorder can be understood through emotional cascades. Emotional cascades are vicious cycles of intense rumination and negative affect that may induce aversive emotional states that generate abnormal behaviors to reduce the effect of intense rumination. Borderline Personality Disorder is a psychiatric disorder whose main symptoms to diagnose it are mood instability and impulsivity. This disorder often involves risky behaviors such as non-suicidal self-injury or substance abuse. Recently, Selby and collaborators have proved that the Emotional Cascade Model has a high explanatory and diagnostic capacity using Temporal Bayesian Networks. Taking into consideration the meta-analytic study developed by Richman et al., in this article it has been designed a deep learning model, based on cascading artificial neural networks, following the correlations established for the Emotional Cascade Model. It has been confirmed with accuracy estimates reaching up to 99%, the predictive power of this model relative to the various types of rumination that influence some of the basic classes of symptoms of Borderline Personality Disorder.

An Emotional Analysis Method Based on Multi Model Ensemble Learning

“…Compared with the traditional machine learning scheme, deep learning technology can automatically and efficiently extract text features and realize the task of emotion classification [15]. At present, there are three mainstream deep learning models: feedforward neural network model, recurrent neural network model and convolutional neural network [16]. Feedforward neural network models are represented by Word2vec and Doc2vec.…”