Healthcare Android apps: a tale of the customers' perspective
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)?
Stanford CoreNLP Christopher Manning, Mihai Surdeanu, John Bauer, Jenny Finkel, Steven Bethard, and David McClosky. 2014. The Stanford CoreNLP natural language processing toolkit. In Proceedings of 52nd annual meeting of the association for computational linguistics: system demonstrations. 55–60
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.
Stanford CoreNLP https://stanfordnlp.github.io/CoreNLP/ and in particular https://nlp.stanford.edu/sentiment/
4. What is the main goal of the whole study?
(i) classifying the user reviews of healthcare open-source apps and (ii) analyzing the sentiment with which users write down user reviews of those apps.
5. What the researchers want to achieve by applying the technique(s) (e.g., calculate the sentiment polarity of app reviews)?
determine sentiment of the user reviews reported by healthcare apps’ users
6. Which dataset(s) the technique is applied on?
1100 user reviews
7. Is/Are the dataset(s) publicly available online? If yes, please indicate their name and links.
It seems that the dataset is not available
8. Is the application context (dataset or application domain) different from that for which the technique was originally designed?
Kind of. Stanford NLP was designed for product reviews, it is not clear to me to what extend app reviews can be seen as product reviews.
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?
N/A
12. Write down any other comments/notes here.
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