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Performance appraisal (PA) is an important HR process to periodically measureand evaluate every employee's performance vis-a-vis the goals established bythe organization. A PA process involves purposeful multi-step multi-modalcommunication between employees, their supervisors and their peers, such asself-appraisal, supervisor assessment and peer feedback. Analysis of thestructured data and text produced in PA is crucial for measuring the quality ofappraisals and tracking actual improvements. In this paper, we apply textmining techniques to produce insights from PA text. First, we perform sentenceclassification to identify strengths, weaknesses and suggestions ofimprovements found in the supervisor assessments and then use clustering todiscover broad categories among them. Next we use multi-class multi-labelclassification techniques to match supervisor assessments to predefined broadperspectives on performance. Finally, we propose a short-text summarizationtechnique to produce a summary of peer feedback comments for a given employeeand compare it with manual summaries. All techniques are illustrated using areal-life dataset of supervisor assessment and peer feedback text producedduring the PA of 4528 employees in a large multi-national IT company.

Mining Supervisor Evaluation and Peer Feedback in Performance Appraisals