Analyzing and automatically labelling the types of user issues that are raised in mobile app reviews

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)?

The paper proposes an approach that can automatically assign multiple labels to reviews of mobile apps based on the raised issues. Several multi-labelling approaches and classifiers have been trained exploiting textual features of terms.

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?

It's two-fold. First, to show through an empirical study that a single review may be related to multiple issues, thus justifying the need for multi-label classification. Second, to propose a solution for this problem.

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

Classify the issues mentioned in app reviews.

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

The study is performed on a set of 226,797 reviews form the App Store and 3,480 from the Google Play store. Of these, 6,390 and 1,066 have been manually labeled, respectively.

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?

By using ten-fold cross validation on the manually labeled dataset.

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, F-Measure micro, F-Measure macro

12. Write down any other comments/notes here.

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