Mining Supervisor Evaluation and Peer Feedback in Performance Appraisals

TL;DR

This paper applies text mining techniques to analyze performance appraisal data, including classification, clustering, and summarization, to extract insights from supervisor and peer feedback in a large corporate setting.

Contribution

It introduces a comprehensive text mining approach for analyzing structured and unstructured PA data, including novel summarization of peer feedback.

Findings

Effective identification of strengths, weaknesses, and suggestions from PA texts

Discovery of broad performance categories through clustering

Automated summarization of peer feedback comments

Abstract

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.