App review analysis via active learning
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)?
active learning for training a classifier
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 exploit active learning for app review classification, which seeks to minimize human effort required for training a review classifier by intelligently selecting unlabelled reviews for labelling via uncertainty sampling.
5. What the researchers want to achieve by applying the technique(s) (e.g., calculate the sentiment polarity of app reviews)?
classify reviews into different categories
6. Which dataset(s) the technique is applied on?
classified app reviews by Maalej
7. Is/Are the dataset(s) publicly available online? If yes, please indicate their name and links.
W. Maalej and H. Nabil, “On the automatic classification of app reviews: Project data,” https://mast.informatik.uni-hamburg.de/ app-review-analysis/
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?
the authors trained two classifiers: Baseline and active learning and test them on dataset metrics: for binary classification: precision, recall, and F 1scores. For multiclass classification, per-class and aggregate measures
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.
-