AR-miner: Mining Informative Reviews for Developers from Mobile App Marketplace
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)?
It proposes AR-Miner, an approach to discriminate between informative and non-informative user reviews in mobile apps. AR-Miner uses a machine learning approach for the classification and then performs clustering to group together similar reviews. The clusters are then ranked by importance based on a number of criteria (e.g., average rating of the cluster).
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 propose an approach for the automatic identification of informative reviews in mobile apps.
5. What the researchers want to achieve by applying the technique(s) (e.g., calculate the sentiment polarity of app reviews)?
Identify informative reviews in mobile apps.
6. Which dataset(s) the technique is applied on?
Reviews related to four Google Play apps. From each of them, 1000 are used for training and 2000 for test.
7. Is/Are the dataset(s) publicly available online? If yes, please indicate their name and links.
Mining Mobile App Marketplace: https://sites.google.com/site/appsuserreviews/
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?
The performance of AR-Miner on the test sets is computed.
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, and Normalized Discounted Cumulative Gain
12. Write down any other comments/notes here.
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