Faizan Ahmad|Ahmed Abbasi|Jingjing Li|David G. Dobolyi|Richard G. Netemeyer|Gari D. Clifford|Hsinchun Chen
A Deep Learning Architecture for Psychometric Natural Language Processing
University of Virginia|University of Virginia|University of Virginia|University of Virginia|University of Virginia|Georgia Institute of Technology|Emory University|University of Arizona
psychometric measures reflecting peoples knowledge ability attitudes personality traits critical many realworld applications ecommerce health care cybersecurity however traditional methods cannot collect measure rich psychometric dimensions timely unobtrusive manner consequently despite importance psychometric dimensions received limited attention natural language processing information retrieval communities article propose deep learning architecture pynda extract psychometric dimensions usergenerated texts pynda contains novel representation embedding demographic embedding structural equation model sem encoder multitask learning mechanism designed work unison address unique challenges associated extracting rich sophisticated usercentric psychometric dimensions experiments three realworld datasets encompassing 11 psychometric dimensions including trust anxiety literacy show pynda markedly outperforms traditional featurebased classifiers well stateoftheart deep learning architectures ablation analysis reveals component pynda significantly contributes overall performance collectively results demonstrate efficacy proposed architecture facilitating rich psychometric analysis results important implications usercentric information extraction retrieval systems looking measure incorporate psychometric dimensions