Ensemble methods for app review classification: An approach for software evolution (n)
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)?
They use machine learning to classify user reviews in mobile apps
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.
Machine learning with textual features extracted from the reviews
4. What is the main goal of the whole study?
To experiment different classifiers and their combination (ensembles) in classifying apps' reviews
5. What the researchers want to achieve by applying the technique(s) (e.g., calculate the sentiment polarity of app reviews)?
Identify the type of information (e.g., bug, suggestion for new feature) in app reviews
6. Which dataset(s) the technique is applied on?
4,550 reviews manually annotated
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
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, the ensembles obtain the best accuracy, with a precision 74% and a recall of 59%
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?
Recall, Precision, F-Measure
12. Write down any other comments/notes here.
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