On measuring affects of github issues' commenters
1. Does the paper propose a new opinion mining approach?
No
2. Which opinion mining techniques are used (list all of them, clearly stating their name/reference)?
machine learning classifier for Emotions: An exploratory qualitative and quantitative analysis of emotions in issue report comments of open source systems. Empirical Software Engineering (2017), 1–44. Danescu et al. for politeness: A computational approach to politeness with application to social factors. In Proceedings of ACL. Sentistrength for sentiment Mäntylä et al for valence, arousal, and dominance: Mining valence, arousal, and dominance: possibilities for detecting burnout and productivity?
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.
politeness tool: http://www.cs.cornell.edu/~cristian/Politeness.html
4. What is the main goal of the whole study?
to compare the affectiveness of the issues’ comments written by users and commenters (who only post comments without posting any issues nor committing changes)
5. What the researchers want to achieve by applying the technique(s) (e.g., calculate the sentiment polarity of app reviews)?
same as 2
6. Which dataset(s) the technique is applied on?
370K comments from 100K issues of 25K users and commenters from 3 open source projects, from GHTorrent
7. Is/Are the dataset(s) publicly available online? If yes, please indicate their name and links.
GHTorrent: https://ghtorrent.org/
8. Is the application context (dataset or application domain) different from that for which the technique was originally designed?
sentistrength is different, others are validated on SE datasets
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?
N/A
10. Does the paper replicate the results of previous work? If yes, leave a summary of the findings (confirm/partially confirms/contradicts).
No
11. What success metrics are used?
N/A
12. Write down any other comments/notes here.
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