A Lens into Employee Peer Reviews Via Sentiment-Aspect Modeling

Given a corpus of employee peer reviews from a large corporation where each review is structured into pros and cons, what are the prevalent traits that employees talk about? How can we describe the performance of an employee with just a few sentences, that help us interpret what their work is praised and criticized for? What is the best way to summarize an employee's reviews, while preserving the content and sentiment as well as possible? In this work, we study a large collection of corporation-wide employee peer reviews from a technology enterprise. Motivated by the challenges we outline in our analysis of employee review data, our work makes two main contributions in the domain of people analytics: (a) Sentiment-Aspect Model (SAM): we introduce a stylized log-linear model that identifies the hidden aspects and sentiment within an employee peer review corpus, (b) Interpretable Sentiment-Aspect Representations (EMPLOYEE2VEC): using SAM, we produce a vector space embedding for each employee, containing an overall sentiment score per aspect, and (c) Summarization of Employee Peer Reviews (PEERSUM): we summarize an employee's peer reviews with just a few sentences which reflect the most prevalent traits and associated sentiment for the employee as much as possible. We show that our model SAM can use the structure present in the dataset as supervision to discover meaningful latent traits and sentiment embodied in the reviews. Our employee vector representations Employee2vecprovide a compact, interpretable overview of their evaluation. The review summaries extracted by PeersUmprovide text that explains the professional performance of an employee in a succinct and objectively quantifiable way. We also show how to use our techniques for people analytics tasks such as the analysis of thematic differences between departments, regions, and genders.