A Comparison of Dictionary Building Methods for Sentiment Analysis in Software Engineering Text
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)?
SentiStrength-SE
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 and the 4 dictionaries
4. What is the main goal of the whole study?
See if the dictionary being used in sentiment analysis has an impact on the performance of sentiment analysis
5. What the researchers want to achieve by applying the technique(s) (e.g., calculate the sentiment polarity of app reviews)?
Compare the performance of SentiStrength-SE when different dictionaries are used
6. Which dataset(s) the technique is applied on?
The one by Murgia et al.
7. Is/Are the dataset(s) publicly available online? If yes, please indicate their name and links.
In theory yes, from the paper by Murgia et al.
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, running SentiStrength-SE with 4 different dictionaries. There is no clear winner here
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, Recall, F-Measure
12. Write down any other comments/notes here.
-