ALDA: An Aggregated LDA for Polarity Enhanced Aspect Identification Technique in Mobile App Domain
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)?
Technique based on LDA to identify aspects through polarity aggregation. Custom made
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.
Textblob
4. What is the main goal of the whole study?
use LDA toe automatically identify and classify relevant reviews
5. What the researchers want to achieve by applying the technique(s) (e.g., calculate the sentiment polarity of app reviews)?
1. classify reviews into informative and non-informative, and 2. identify key aspects present in reviews
6. Which dataset(s) the technique is applied on?
labeled dataset of 9000 reviews from R-miner: Chen, N., Lin, J., Hoi, S.C., Xian, X., Zhang, B.: AR-miner: mining informative reviews for developers from mobile app marketplace. In: Proceesings of the 36th International Conference on Software Engineering, pp. 767–778. ACM (2014)
7. Is/Are the dataset(s) publicly available online? If yes, please indicate their name and links.
yes
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?
Prec/Rec, Acc, F-m. Verification through golden set
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?
Prec/Rec, Acc, F-m
12. Write down any other comments/notes here.
highly relevant