Feature-based Evaluation of Competing 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)?
SentiStrength --> Thelwall, M. et al. 2010. Sentiment strength detection in short informal text. Journal of the American Society for Information Science and Technology. 61, 12 (Dec. 2010), 2544–2558.
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.
SentiStrength: http//sentistrength.wlv.ac.uk/
4. What is the main goal of the whole study?
Extract features and sentiment expressed in app reviews such that developers can more easily understand the opinion of users.
5. What the researchers want to achieve by applying the technique(s) (e.g., calculate the sentiment polarity of app reviews)?
Calculate the sentiment of app reviews.
6. Which dataset(s) the technique is applied on?
App review dataset of Swinburne University. Contains app reviews for 25 apps.
7. Is/Are the dataset(s) publicly available online? If yes, please indicate their name and links.
yes: Swinburne University app reviews: http://researchbank.swinburne.edu.au/vital/access/manager/Repository/swin:35267
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.
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?
-
12. Write down any other comments/notes here.
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