A multidimensional approach to predicting action probability from text

Abstract

Predicting the probability of action from textual data has been an area of growing interest in various domains, including social media analysis, customer support, psychology, criminology, and online marketing. Our paper proposes a novel approach that combines multiple text representation techniques, ensemble methods, and a multidimensional linguistic analysis framework to predict the probability of action from text data. The approach consists of five main phases: data preprocessing, text representation, feature fusion and selection, multidimensional analysis and action probability prediction.