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

1. Does the paper propose a new opinion mining approach?

Yes

2. Which opinion mining techniques are used (list all of them, clearly stating their name/reference)?

The paper introduces a lexicon based method for mining valence and arousal.

3. Which opinion mining approaches in the paper are publicly available? Write down their name and links. If no approach is publicly available, leave it blank or None.

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4. What is the main goal of the whole study?

Determine how valence and arousal occur in JIRA issues to be able to assess the health of developers.

5. What the researchers want to achieve by applying the technique(s) (e.g., calculate the sentiment polarity of app reviews)?

Calculate the valence and arousal in JIRA issue comments.

6. Which dataset(s) the technique is applied on?

Ortu's JIRA dataset.

7. Is/Are the dataset(s) publicly available online? If yes, please indicate their name and links.

JIRA dataset: M. Ortu, G. Destefanis, B. Adams, A. Murgia, M. Marchesi, and R. Tonelli. The JIRA Repository Dataset. In Proceedings of the 11th International Conference on Predictive Models and Data Analytics in Software Engineering - PROMISE ’15, pages 1–4, New York, USA, 2015. ACM Press.

8. Is the application context (dataset or application domain) different from that for which the technique was originally designed?

Yes, the lexicon used is not SE specific.

9. Is the performance (precision, recall, run-time, etc.) of the technique verified? If yes, how did they verify it and what are the results?

No

10. Does the paper replicate the results of previous work? If yes, leave a summary of the findings (confirm/partially confirms/contradicts).

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11. What success metrics are used?

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12. Write down any other comments/notes here.

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