  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.
