Usability Related Qualities Through Sentiment Analysis

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)?

Part-of-speech tagging: to find relevant qualities, KWIC (Keyword in Context): to extract fragments of text where the keywords appear

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 find interdependencies among non-functional requirements.

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

to first identify the related qualities, and find their interdependency

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

123 texts of github issues

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

https://github.com/nitanilla/Usability-Related-Qualities-through-Sentiment-Analysis/

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?

No, just compared the keywords qualified by sentistrength

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.

ERA