An Approach of Extracting Feature Requests from App 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)?
J48, Naive Bayes, Random Forest, and SVM (sentiment by sentistrength as a feature); LDA; Stanford Parser
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 semi-automated approach to extract feature requests based on machine learning approaches
5. What the researchers want to achieve by applying the technique(s) (e.g., calculate the sentiment polarity of app reviews)?
classifiers: to identify which reviews belong to feature requests LDA: cluster reviews on feature requests Stanford Parser: generate word dependencies of sentences, based on the clustered topics and terms
6. Which dataset(s) the technique is applied on?
reviews from the AppleStore and the Google Play stores from Maalej, W., Nabil, H.: Bug report, feature request, or simply praise? On automatically classifying app reviews.
7. Is/Are the dataset(s) publicly available online? If yes, please indicate their name and links.
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?
only classification is verified with precision, recall, F-measure and the execution time under equal scale of training and testing data were calculated for five times: When BW, LR and metadata are used as classification attributes, Naive Bayes can get the best results and both of its precision and recall can reach 82.4%. Additionally, the executing time of Naive Bayes is the shortest.
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.
-