A Multi-label Active Learning Approach for Mobile App User Review Classification

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)?

analysis of emojis, self made, and Textblob

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 - https://textblob.readthedocs.io/en/dev/quickstart.html

4. What is the main goal of the whole study?

Classification of mobile app user reviews

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

to use multi-label active learning to build a classifier

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

custom dataset of 10982 reviews obtained through crawling

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?

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?

F-measure and comparison with other approaches (KNN, MLP, RF, TREE)

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?

F-Measure and Hamming Loss

12. Write down any other comments/notes here.

their dictionary of 248 emojis split into positive, neutral and negative is not available.