Automatic Classification of Apps Reviews for Requirement Engineering: Exploring the Customers Need from Healthcare Applications

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)?

Naive Bayes, Naive Bayes Multinomial, Random Forest, SVM

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 classify a set of reviews about healthcare-domain applications into multiple types of categories such as bug reports, new feature requests, application performance, and user interface

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

to classify reviews into bug (general, Security, Usability, Performance), new feature, sentimental (pos, neu, neg)

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

7500 reviews of ten different health-related mobile applications

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?

retrained

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?

Precision, recall, f1. Multi-nominal Naive Bays algorithm has shown the best performance

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.

-