DEVA: Sensing Emotions in the Valence Arousal Space in Software Engineering Text

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)?

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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.

The authors introduce a new technique for valence and arousal. Their tool is publicly available: https://figshare.com/s/277026f0686f7685b79e.

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

Introduce and evaluate an opinion mining tool.

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

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

A manually annotated dataset introduced in the paper.

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

JIRA data: https://figshare.com/s/277026f0686f7685b79e.

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

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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?

Yes, by running the tool on a manually labeled dataset. Showing an average results of precision: 82%, recall: 78% and F1: 80%. Moreover, they compare to TensiStrength.

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?

Precision, recall, F1.

12. Write down any other comments/notes here.

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