Automatic Evaluation of Employee Satisfaction
Authors: Marco Piersanti, Giulia Brandetti, Pierluigi Failla (Enel Italia S.R.L., 2017)
Topic: Sentiment, Emotion, Irony, Hate

Abstract
Human Resources are one of the most important assets in modern organizations. Their capability of facing employees' needs is critical in order to have an effective and efficient company, where people are the center of all business processes. This work is focused on developing new techniques that, leveraging a data driven approach, can help Human Resources to find a more precise employee satisfaction categorization, to easily identify possible issues and to act in a proactive fashion.

Introduction
Every modern organization has a dedicated function which takes care of its employees, commonly called Human Resources (HR). HR duties are related to the capability of creating value through people, ensuring that everyone can express his own potential and has a productive and comfortable office environment. Nowadays, HR can rely on data to create a new paradigm based on a data driven approach, where analysts can leverage data in order to get more complete, detailed and data-supported decisions. Being able to monitor employees' engagement and satisfaction is critical in order to maintain a positive and constructive office environment. The benefit for the company is in the capability of retaining the best employees and keeping the overall workforce strong and motivated. Furthermore, recent surveys AUTHOR show the issues that companies are facing when they try to do retention or improve engagement. This paper is organized as follows. Section sec:rel_work presents a literature review on both themes of HR Management and text mining, Section sec:task_desc summarizes the motivations that drove the present study, Sections sec:exp_data and sec:model_desc discuss data and methodology, respectively, and Section sec:results presents the results. Finally, Section sec:concl discusses the implications of the findings and further possible developments.

Conclusion
The proposed approach could be a powerful tool for HR-BPs to better understand the main issues related to the lack of employees' satisfaction. Furthermore, it could help HR analysts to quickly decide which are the best actions to solve those issues, analyzing whether a complaint is isolated or shared by a group, whether it's trivial or urgent and act accordingly. As an example, HR Departments could test different actions over a group of unsatisfied employees, in order to understand which one is the most effective for a given issue. The very same model could also be used on neutral and positive subjects, so that HR could check whether the quality of life at work of these employees could be somehow improved, and understand which are the essential key factors for the employees' well-being.%, allowing the building of a positive working environment. From a technical point of view, one possible improvement in order to strengthen the solidity of the present approach could be to manually annotate a subset of (anonymized) texts, developing a gold standard of HR interview clusters, to be used as a test set for techniques like the one presented in this study. This gold standard may be made available company-wise, in order to encourage collaboration and to foster the creation of a data science community, to help bring a data driven way of thinking even to those areas which have been traditionally more reluctant to adopt a rigorous digital transformation. This is a first step to improve how HR Departments operate nowadays. We strongly believe that the introduction of a data driven approach can support critical HR decisional processes and improve companies' productivity, without having to sacrifice each individual's quality of life.