A Linguistic Analysis to Quantify Over-Explanation and Under-Explanation in Job Interviews

Abstract

Receiving insight into the thoughts and feelings of a recruiter is vital to understanding effective job interviews. To ascertain categorical responses and speech patterns, audio and visual data from mock job interviews were collected between interviewees and company representatives. From the study, extracted features of audio and visual data were compiled. As a result, several approaches involving deep learning were leveraged to infer the probability of an over-explained or under-explained snippet of text.