Analysis of Newcomers Activity in Communicative Posts on GitHub
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)?
The VADER sentiment analysis tool, a dictionary-based sentiment analysis tool tailored for comments (thus, handling for example emojis).
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.
VADER: https://github.com/cjhutto/vaderSentiment
4. What is the main goal of the whole study?
The goal of the paper is to investigate relations towards newcomers through sentiment analysis of comments they receive in issues and pull requests in GitHub repositories. They analyze differences between reactions to contributions of ‘old’ and ‘new’ developers, and find that while the majority of comments is rated as neutral, the amount of negativity is slightly higher for newcomers.
5. What the researchers want to achieve by applying the technique(s) (e.g., calculate the sentiment polarity of app reviews)?
Calculate the sentiment polarity of comments in issues and PRs.
6. Which dataset(s) the technique is applied on?
2 381 800 comments for top-10 open source projects by contributor count and 761 893 comments for top-10 fastest growing open source projects
7. Is/Are the dataset(s) publicly available online? If yes, please indicate their name and links.
No
8. Is the application context (dataset or application domain) different from that for which the technique was originally designed?
Yes
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).
No
11. What success metrics are used?
None
12. Write down any other comments/notes here.
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