Pattern-based Mining of Opinions in Q\&\#38;A Websites
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)?
POME, novel approach using NLP and pattern matching
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.
Everything available as replication package: https://pome-repo.github.io
4. What is the main goal of the whole study?
-
5. What the researchers want to achieve by applying the technique(s) (e.g., calculate the sentiment polarity of app reviews)?
1. Compare the pattern-matching approach with ML leveraging the patterns as well as n-grams extracted from SO; 2. assess the ability of POME to detect the polarity of sentences, as compared to sentiment-analysis tools; 3. compare POME with the state-of-the-art SO opinion mining approach, Opiner
6. Which dataset(s) the technique is applied on?
Manually built dataset of 4,346 sentences from SO
7. Is/Are the dataset(s) publicly available online? If yes, please indicate their name and links.
Yes, POME replication package: https://pome-repo.github.io
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?
Yes, comparison through precision and recall with the state-of-the-art approaches. POME greatly outperforms
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?
Precision and Recall
12. Write down any other comments/notes here.
-