Bootstrapping a Lexicon for Emotional Arousal in Software Engineering

TL;DR

This paper introduces SEA, a specialized lexicon for measuring emotional arousal in software engineering, built via bootstrapping from issue data and manual scoring, which helps differentiate issue priorities related to emotional activation.

Contribution

The paper presents the first software engineering-specific arousal lexicon, SEA, created through a novel bootstrapping method combining word embeddings and manual scoring.

Findings

SEA differentiates issue priorities effectively

Combining SEA with Warriner et al.'s lexicon improves performance

Achieves Cohen's d effect sizes up to 0.5

Abstract

Emotional arousal increases activation and performance but may also lead to burnout in software development. We present the first version of a Software Engineering Arousal lexicon (SEA) that is specifically designed to address the problem of emotional arousal in the software developer ecosystem. SEA is built using a bootstrapping approach that combines word embedding model trained on issue-tracking data and manual scoring of items in the lexicon. We show that our lexicon is able to differentiate between issue priorities, which are a source of emotional activation and then act as a proxy for arousal. The best performance is obtained by combining SEA (428 words) with a previously created general purpose lexicon by Warriner et al. (13,915 words) and it achieves Cohen's d effect sizes up to 0.5.