Classification of Mobile Application User Reviews for Generating Tickets on Issue Tracking System
1. Does the paper propose a new opinion mining approach?
No
2. Which opinion mining techniques are used (list all of them, clearly stating their name/reference)?
different classification approaches including BOW, doc2vec, ExtraTreesClassifier, stopwords, lemmatization, VotingClassifier, LogisticRegression, LinearSVC, sentiScore, GaussianNB, etc
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.
-
4. What is the main goal of the whole study?
to investigate different machine learning algorithms to classify bug reports and feature requests that appeared in mobile application user reviews in the App Store and Play Store
5. What the researchers want to achieve by applying the technique(s) (e.g., calculate the sentiment polarity of app reviews)?
to classify bug reports and feature requests
6. Which dataset(s) the technique is applied on?
app-review-analysis data by Maalej
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?
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?
yes, with precision, recall, f1, accuracy
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.
-