Same app, different app stores: A comparative study

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)?

Naive Bayes (NB) and Support Vector Machines (SVM)

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 analyze discrepancies and root causes of user complaints to understand cross-platform development challenges that impact cross-platform user-perceived ratings

5. What the researchers want to achieve by applying the technique(s) (e.g., calculate the sentiment polarity of app reviews)?

to classify 1.7 million textual user reviews obtained from 2,000 of the mined app-pairs

6. Which dataset(s) the technique is applied on?

A dataset of 80,169 cross-platform app-pairs (iOS/Android), extracted by analyzing the properties of 2.4M apps from the Google Play and Apple app stores

7. Is/Are the dataset(s) publicly available online? If yes, please indicate their name and links.

https://github.com/saltlab/Minning-App-Stores

8. Is the application context (dataset or application domain) different from that for which the technique was originally designed?

retrained The training and testing data for our classifiers were randomly composed of 1,575 and 525 of the manually labelled reviews

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, SVM achieves a higher F-measure. On average, F(SVM) = 0.84 for the generic classifier and F(SVM) = 0.74 for the sentiment classifier.

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?

F-measure

12. Write down any other comments/notes here.

-