SEntiMoji: An Emoji-powered Learning Approach for Sentiment Analysis in Software Engineering
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)?
SEntiMoji, a novel approach
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.
dataset is available, the approach itself is not
4. What is the main goal of the whole study?
Sentiment analysis through the mining of emojis
5. What the researchers want to achieve by applying the technique(s) (e.g., calculate the sentiment polarity of app reviews)?
Compare Sentimoji to existing sentiment analysis approaches on a common benchmark
6. Which dataset(s) the technique is applied on?
Datasets: JIRA (2573 issue comments), SO (4,423 posts), Code Review (1,600 review comments), Java Library (1,500 sentences)
7. Is/Are the dataset(s) publicly available online? If yes, please indicate their name and links.
https://github.com/SEntiMoji/SEntiMoji
8. Is the application context (dataset or application domain) different from that for which the technique was originally designed?
No, ad hoc technique
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 to others through precision, recall and F-Score. Sentimoji outperforms largely the others
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?
McNemar test for comparison
12. Write down any other comments/notes here.
-