EmoD: An End-to-End Approach for Investigating Emotion Dynamics in Software Development

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)?

EmoTXT is used to identify emotions (anger, fear, joy, and sadness)

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.

EmoTXT: https://github.com/collab-uniba/Emotion_and_Polarity_SO Emotion Intensity Lexicon: https://saifmohammad.com/WebPages/NRC-Emotion-Lexicon.htm

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

The paper presents EMOD, a tool to automatically collect project teams’ communication records, identify the emotions and their intensities in them, model the emotion dynamics into time series, and provide efficient data management.

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

Calculate the sentiments in developers' communications

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

Comments from GitHub generated by 10 code contributors and 340 other contributors.

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?

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, it's a sort of tool demo paper.

12. Write down any other comments/notes here.

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