# Mining Valence, Arousal, and Dominance - Possibilities for Detecting Burnout and Productivity?

arXiv: https://arxiv.org/abs/1603.04287 · HTML: https://mlsanity.com/paper/1603.04287/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Mar 2016.

## Built on by (most cited first, 6 of 6)

- [Emoji Promotes Developer Participation and Issue Resolution on GitHub](https://mlsanity.com/paper/2308.16360/) (2023)
- [An Empirical Study of the Effectiveness of an Ensemble of Stand-alone Sentiment Detection Tools for Software Engineering Datasets](https://mlsanity.com/paper/2111.03196/) (2021)
- [On Technical Trading and Social Media Indicators in Cryptocurrencies' Price Classification Through Deep Learning](https://mlsanity.com/paper/2102.08189/) (2021)
- [Ousiometrics: The essence of meaning aligns with a power-danger-structure framework instead of valence-arousal-dominance](https://mlsanity.com/paper/2110.06847/) (2021)
- [Machine Learning Models for the Early Detection of Burnout in Software Engineering: a Systematic Literature Review](https://mlsanity.com/paper/2603.23063/) (2026)
- [BurnRiSc: Toward Non-Invasive Burnout Screening in Open Source from Public Repository Signals](https://mlsanity.com/paper/2609.19422/) (2026)

## Abstract (arXiv)

Similar to other industries, the software engineering domain is plagued by psychological diseases such as burnout, which lead developers to lose interest, exhibit lower activity and/or feel powerless. Prevention is essential for such diseases, which in turn requires early identification of symptoms. The emotional dimensions of Valence, Arousal and Dominance (VAD) are able to derive a person's interest (attraction), level of activation and perceived level of control for a particular situation from textual communication, such as emails. As an initial step towards identifying symptoms of productivity loss in software engineering, this paper explores the VAD metrics and their properties on 700,000 Jira issue reports containing over 2,000,000 comments, since issue reports keep track of a developer's progress on addressing bugs or new features. Using a general-purpose lexicon of 14,000 English words with known VAD scores, our results show that issue reports of different type (e.g., Feature Request vs. Bug) have a fair variation of Valence, while increase in issue priority (e.g., from Minor to Critical) typically increases Arousal. Furthermore, we show that as an issue's resolution time increases, so does the arousal of the individual the issue is assigned to. Finally, the resolution of an issue increases valence, especially for the issue Reporter and for quickly addressed issues. The existence of such relations between VAD and issue report activities shows promise that text mining in the future could offer an alternative way for work health assessment surveys.