Statistical affect detection in collaborative chat

Abstract

Geographically distributed collaborative teams often rely on synchronous text-based online communication for accomplishing tasks and maintaining social contact. This technology leaves a trace that can help researchers understand affect expression and dynamics in distributed groups. Although manual labeling of affect in chat logs has shed light on complex group communication phenomena, scaling this process to larger data sets through automation is difficult. We present a pipeline of natural language processing and machine learning techniques that can be used to build automated classifiers of affect in chat logs. Interpreting affect as a dynamic, contextualized process, we explain our development and application of this method to four years of chat logs from a longitudinal study of a multi-cultural distributed scientific collaboration. With ground truth generated through manual labeling of affect over a subset of the chat logs, our approach can successfully identify many commonly occurring types of affect.