Mining non-functional requirements from App store reviews

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)?

VADER

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://www.nltk.org/howto/sentiment.html

4. What is the main goal of the whole study?

Extract non-functional feature requests from app reviews.

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

Calculate sentiment polarity of sentences in app reviews.

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

A dataset of app reviews scraped from the iOS app store, stratified for app category.

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

Yes: http://seel.cse.lsu.edu/data/emse19.zip

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

Yes, vader was not designed for app 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?

No.

10. Does the paper replicate the results of previous work? If yes, leave a summary of the findings (confirm/partially confirms/contradicts).

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11. What success metrics are used?

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12. Write down any other comments/notes here.

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