Ensemble Methods for App Review Classification: An Approach for Software Evolution
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)?
Machine learning approaches used to classify mobile app reviews. Also, an ensemble method is used to combine multiple classifiers.
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 experiment different machine learning techniques, including ensemble methods, for the classification of app reviews.
5. What the researchers want to achieve by applying the technique(s) (e.g., calculate the sentiment polarity of app reviews)?
Classifying the type of review (e.g., bug, feature strength, etc.)
6. Which dataset(s) the technique is applied on?
User reviews of seven apps, three from Apple App store, four from Google Play, for a total of 4550 reviews.
7. Is/Are the dataset(s) publicly available online? If yes, please indicate their name and links.
Manually labeled app reviews: https://ase.in.tum.de/lehrstuhl_1/images/publications/Emitza_Guzman_Ortega/truthset.tsv
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, with a ten-fold cross validation.
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.
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