The Value of Structured and Unstructured Content Analytics of Employees’ Opinion Mining

Abstract

Employees and their knowledge are critical for a company’s success and were extensively covered by literature. Free form of knowledge, as opinions or feedback is analyzed in some companies in order to understand potential areas of improvement or satisfaction. This research focuses on analyzing free format opinions collected with a survey instrument from 586 employees of a bank. Various techniques as text analytics, word embedding, supervised and unsupervised learning were explored, extracting key concepts or entities. Attribution and relational similarity was explored using techniques as supervised text classification or unsupervised, by word vector representation and embedding using word2vdec. The aim was to explain overall satisfaction combining structured data (collected with closed questions survey, using 7 points Likert scale) enhanced with text classification of opinions, related to best and worst applications, along with explanations. The theoretical model used for ontology and taxonomy of text classification was based on Technology Acceptance Model, mapped into the main perceptual constructs, Perceived Ease of Use and Perceived Utility. The scope of research was the banks’ enterprise IT environment, not focused on a specific application. Various quantitative models, including linear models, decision trees and neural nets, were evaluated to capture potential causality of overall employee IT satisfaction level. The results suggest strong influence of perceptual constructs towards satisfaction, within all methods, while unstructured textual data provide additional insights on employees’ perception from concept associations.