Tool Support for Analyzing Mobile App Reviews

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)?

Rather than actual opinion mining, MARK (the proposed tool) supports annotation and retrieval of opinions about apps by relying on a set of pre-defined 100 negative words combined with word2vec for clustering of similar word occurring in the review corpus. Browsing the word clusters, the users select the one related to the aspect he/she is interested in. The user might also add new keywords.

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?

Presenting a tool that supports opinion annotation for apps.

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

Supporting the annotation and analysis of reviews by leveraging distributional semantics and a list of predefined negative seed words.

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

app reviews

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?

yes

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?

NA

12. Write down any other comments/notes here.

-