A comparison of software engineering domain specific sentiment analysis tools
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)?
sentiment analysis
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.
SentiStrength-SE, Senti4SD EmoTxT: https://github.com/collab-uniba/Emotion_and_Polarity_SO
4. What is the main goal of the whole study?
To compare approaches for sentiment analysis that are tailored to the SE domain on different datasets.
5. What the researchers want to achieve by applying the technique(s) (e.g., calculate the sentiment polarity of app reviews)?
Calculate the sentiment polarity of software-related text, and in particular issue comments, Stack Overflow posts, and code review comments.
6. Which dataset(s) the technique is applied on?
JIRA issue comments (5,576), Stack Overflow posts (4,423), and Code Review Comments (2,000)
7. Is/Are the dataset(s) publicly available online? If yes, please indicate their name and links.
Jira issue comments (JIC): https://ansymore.uantwerpen.be/system/files/uploads/artefacts/alessandro/%20MSR16/archive3.zip Stack Overflow posts: https://github.com/collab-uniba/Emotion_and_Polarity_SO Code Review comments: https://github.com/senticr/SentiCR
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, by running the three approaches on the three datasets.
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?
Recall, Precision, FM
12. Write down any other comments/notes here.
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