Emerging app issue identification from user feedback: experience on WeChat

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)?

NLP is applied to identify emerging issues in apps' user reviews. After a preprocessing step, emerging words are are detected and then clustered.

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 identify emerging issues in mobile apps in order to timely notify the developers.

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

Find issues mentioned in reviews.

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

181,679 reviews from WeChat

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, precision/recall/F-measure.

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.

-