CROSA: Context-aware cloud service ranking approach using online reviews based on sentiment analysis
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)?
A custom lexicon based technique (nameless) that uses a dictionary based approach to first extract features, and then based on the first determines sentiment.
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.
-
4. What is the main goal of the whole study?
Automatically process online cloud service reviews in a context aware manner such that practitioners can more easily distinguish service cloud service providers based on a so-called service measurement index.
5. What the researchers want to achieve by applying the technique(s) (e.g., calculate the sentiment polarity of app reviews)?
Determine sentiment expressed per feature in online reviews.
6. Which dataset(s) the technique is applied on?
Cloud service reviews, and Software reviews.
7. Is/Are the dataset(s) publicly available online? If yes, please indicate their name and links.
Trustradius: Software reviews, software comparisons and more. https://www.trustradius.com/. Business software and services reviews | G2 crowd. https://www.g2crowd.com/.
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?
No
10. Does the paper replicate the results of previous work? If yes, leave a summary of the findings (confirm/partially confirms/contradicts).
-
11. What success metrics are used?
-
12. Write down any other comments/notes here.
-