Mining Supervisor Evaluation and Peer Feedback in Performance Appraisals
This work addresses the problem of analyzing performance appraisal text for HR professionals, but it is incremental as it applies existing text mining methods to a new domain-specific dataset.
The paper applied text mining techniques to analyze supervisor assessments and peer feedback from performance appraisals of 4528 employees in a large IT company, identifying strengths, weaknesses, and suggestions through classification and clustering, and proposing a summarization method for peer feedback.
Performance appraisal (PA) is an important HR process to periodically measure and evaluate every employee's performance vis-a-vis the goals established by the organization. A PA process involves purposeful multi-step multi-modal communication between employees, their supervisors and their peers, such as self-appraisal, supervisor assessment and peer feedback. Analysis of the structured data and text produced in PA is crucial for measuring the quality of appraisals and tracking actual improvements. In this paper, we apply text mining techniques to produce insights from PA text. First, we perform sentence classification to identify strengths, weaknesses and suggestions of improvements found in the supervisor assessments and then use clustering to discover broad categories among them. Next we use multi-class multi-label classification techniques to match supervisor assessments to predefined broad perspectives on performance. Finally, we propose a short-text summarization technique to produce a summary of peer feedback comments for a given employee and compare it with manual summaries. All techniques are illustrated using a real-life dataset of supervisor assessment and peer feedback text produced during the PA of 4528 employees in a large multi-national IT company.