Auto-encoder based cognitive analysis of questionnary, Oto-kodlayicilarla anketlerin bilişsel analizi

Abstract

In this study we propose a new method for Likert scale questionnary data analysis using auto-encoders. The proposed method extracts the patterns, which maximally activate the neurons of the hidden layer in the auto-encoder, trained by the questionnary data. These patterns are thought to be cognitive patterns that are influenced by participants filling the questionnary. In the experiments, we employ a questionnary designed to measure the confidence level of a blog author on the web. The cognitive patterns obtained in the auto-encoder are considered as the components that form the general approaches of the participants. In two cognitive patterns drawn from the questionnary, it is observed that the blogger's expertise, integrity, benevolance are evaluated in decreasing or increasing order by the participants. It has also been observed that the proposed method can be used to correct the unintentional mistakes in questionnary answers. © 2018 Elsevier B.V., All rights reserved.