Studying the consistency of star ratings and reviews of popular free hybrid Android and iOS apps
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)?
TwitterLDA
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.
https://github.com/minghui/Twitter-LDA
4. What is the main goal of the whole study?
to study whether these hybrid development tools (using single codebase across platforms) achieve their main purpose: delivering an app that is perceived similarly by users across platforms
5. What the researchers want to achieve by applying the technique(s) (e.g., calculate the sentiment polarity of app reviews)?
to extract discussion topics from all 1 to 5-star reviews for the identified cross-platform hybrid apps, with a goal to find out what users discuss on cross-platform hybrid apps
6. Which dataset(s) the technique is applied on?
68 hybrid app-pairs
7. Is/Are the dataset(s) publicly available online? If yes, please indicate their name and links.
basic app info of the 68 apps https://zenodo.org/record/1181881#.X2aXNmozbPY
8. Is the application context (dataset or application domain) different from that for which the technique was originally designed?
unsupervised
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?
No
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.
-