Automatic Mining of Opinions Expressed About APIs in Stack Overflow
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)?
SentiCR, SentistrengthSE, Senti4SD ,SentiSTrength, OpinerDSO, OpinerDSOSenti. Last two are not publicly available.
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.
SentiCR: https://github.com/senticr/SentiCR SentistrengthSE: https://laser.cs.uno.edu/Projects/Projects.html Senti4SD: https://github.com/collab-uniba/Senti4SD SentiStrength: http://sentistrength.wlv.ac.uk/
4. What is the main goal of the whole study?
Design a system that can extract opinions expressed about API's on Stackoverflow and present this to users of the system.
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 sentences expressing an opinion about aspects of API's.
6. Which dataset(s) the technique is applied on?
A handlabeled dataset of opinionated sentences on StackOverflow.
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?
For SentiCR, SentiStrengthSE, SentiStrength, and Senti4SD yes. For OpinerDSO, and OpinerDSOSenti 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?
Yes, authors verified all five tools on the dataset. Result are subdivided over positive, negative, neutral categories, and, macro and micro averages. The results show that both OpinerDSO and Senti4SDare relatively high scoring tools.
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?
Precision, Recall and F1.
12. Write down any other comments/notes here.
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