What People Like in Mobile Finance Apps: An Analysis of User Reviews
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)?
NLTK Vader
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.
VADER: https://www.nltk.org/_modules/nltk/sentiment/vader.html
4. What is the main goal of the whole study?
Analyze what features user like and don't like in mobile financial apps.
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 of mobile finance apps.
6. Which dataset(s) the technique is applied on?
App reviews scraped from apps that have both an android and iOS version, are frequently downloaded, and are in the finance category.
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?
Sort of, authors verify whether there is agreement between star rating and sentiment score of sentences of a review. Finding that such agreement exists (they inspect a bar plot).
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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