Please use this identifier to cite or link to this item:
http://elea.unisa.it/xmlui/handle/10556/6434
Title: | Corporate profiling: text analytics for employability skills mining |
Authors: | Smaldone, Francesco Antonelli, Valerio Pellicano, Marco |
Keywords: | Big data analytics;Corporate profiling;Text mining |
Issue Date: | 2-Nov-2021 |
Publisher: | Universita degli studi di Salerno |
Abstract: | In the current scenario, the increasing attention to big data realities is causing firms to develop new tools for corporate management, touching several realities from the marketing compart to the HR management. In the context of today’s rapid technological development and its application in a growing array of fields, the role of big data is simultaneously evolving. The present doctoral thesis provides insights into the current expectations of employers seeking to hire individuals. Profiling was conducted by harvesting relevant data from job ads published in a US employment website, which currently attracts the US market's highest recruitment traffic. This research aims to identify the skills, experience, and qualifications sought by employers in several industries and for several professionals, also indicating to candidates the tangible parameters that would increase their employability in such a role. [edited by Author] |
Description: | 2019 - 2020 |
URI: | http://elea.unisa.it:8080/xmlui/handle/10556/6434 http://dx.doi.org/10.14273/unisa-4506 |
Appears in Collections: | Big Data Management |
Files in This Item:
File | Description | Size | Format | |
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tesi di dottorato F. Smaldone.pdf | tesi di dottorato | 20 MB | Adobe PDF | View/Open |
abstract in inglese e in italiano F. Smaldone.pdf | abstract in italiano e in inglese a cura dell'Autore | 135,25 kB | Adobe PDF | View/Open |
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