Monitoring sentiment in open source mailing lists: exploratory study on the apache ecosystem
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Large software projects, both open and closed source, are constructed and maintained collaboratively by teams of developers and testers, who are typically geographically dispersed. This dispersion creates a distance between team members, hiding feelings of distress or (un)happiness from their manager, which prevents him or her from using remediation techniques for those feelings. This paper evaluates the usage of automatic sentiment analysis to identify distress or happiness in a development team. Since mailing lists are one of the most popular media for discussion in distributed software projects, we extracted sentiment values of the user and developer mailing lists of two of the most successful and mature projects of the Apache software foundation. The results show that (1) user and developer mailing lists carry both positive and negative sentiment and have a slightly different focus, while (2) work is needed to customize automatic sentiment analysis techniques to the domain of software engineering, since they lack precision when facing technical terms
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it