Automatic classification of non-functional requirements from augmented app user reviews
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)?
combined four classification techniques BoW, TF-IDF, CHI2 , and AUR-BoW (proposed in this work) with three machine learning algorithms Naive Bayes, J48, and Bagging
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 automatically classify user reviews into four types of NFRs (reliability, usability, portability, and performance), Functional Requirements (FRs), and Others
5. What the researchers want to achieve by applying the technique(s) (e.g., calculate the sentiment polarity of app reviews)?
same as 4
6. Which dataset(s) the technique is applied on?
4000 user review sentences were randomly sampled and manually classified from iBooks (iOS) and Whatsapp (Android) (2000 each)
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?
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?
10-fold cross-validation, AUR-BoW with Bagging achieves the best result (a precision of 71.4%, a recall of 72.3%, and an Fmeasure of 71.8%)
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, f-measure
12. Write down any other comments/notes here.
-