Combining goal model with reviews for supporting the evolution of 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)?

Pattern.en

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.

Pattern.en: https://www.clips.uantwerpen.be/pattern

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

Link reviews on the app store to a goal model of apps such that more specific feedback can be extracted from reviews and communicated to developers.

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

Extract the sentiment expressed about a particular 'goal' in an app store review.

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

A manually scraped set of several 100k reviews of 6 different apps taken from 6 different categories.

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

Yes: https://github.com/hxmarshx/

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

Yes.

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, authors only verify precision of the entire pipeline, not the sentiment mining performed.

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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