Analyzing and Providing Comprehensive Feedback for French CVS with Readerbench
Gabriel Guțu-Robu, Paraschiv, Ionut Cristian, Mihai Dascălu, Cristian, Gabriel, Ștefan Trăușan-Matu, Olivier Lepoivre · DSpace (Open University in the Netherlands) · 2018
In their everyday activities, recruiters are faced with the difficult task of analyzing and judging the quality of a wide range of CVs. Both the content quality and the visual hues, such as colors and their overall structure, need to be considered. This article enhances previous researches with a larger dataset, refined indices, and a more advanced technique of parsing the input documents. After applying various processing techniques from ReaderBench, an advanced Natural Language Processing framework, on a manually annotated dataset of 96 positive and negative French CVs, several writing indices were determined and filtered by leveraging statistical analyses. In addition, our experiment introduces a web application in which users can submit, gather an evaluation, and acquire valuable feedback on their CV.