Automatically classifying user requests in crowdsourcing requirements engineering
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)?
KNN, NB, SVM, with non-project & project specific keywords, heuristic properties of user requests as features
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.
None
4. What is the main goal of the whole study?
To propose a keywords-based machine approach to semi-automatically classify user requests in crowdsourcing scenarios
5. What the researchers want to achieve by applying the technique(s) (e.g., calculate the sentiment polarity of app reviews)?
to classify user requests into seven different properties
6. Which dataset(s) the technique is applied on?
feature requests of three projects from sourceforge.net: KeePass, Mumble and Winmerge.
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, retrained
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?
precision, recall, f-measure
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?
N/A
12. Write down any other comments/notes here.
-