MEME: Toward a Method for Emotions Extraction from Github

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)?

MEME, a Method for EMotion Extraction from GitHub, proposed in this paper.

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?

To present an approach for extracting emotions from software repositories.

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

Be able to extract emotions from GitHub comments.

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

A dataset of comments from 1134 GitHub 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?

No

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?

Not really. There is a sort of qualitative analysis against R packages for emotions identification in text. However, no clear results are reported for what concerns the performance of the approach.

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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