Speech-acts based analysis for requirements discovery from online discussions
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)?
Speech-acts based technique (rule-based annotation of speech acts, then ML techniques to classify)
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.
ARFF file, which can be fed into Weka tool and train classifier models.. http://se.fbk.eu/technologies/speech-acts-based-analysis
4. What is the main goal of the whole study?
to exploit a linguistic technique based on speech-acts for the analysis of online discussions with the ultimate goal of discovering requirements-relevant information
5. What the researchers want to achieve by applying the technique(s) (e.g., calculate the sentiment polarity of app reviews)?
to extract requirements-related information from online discussions
6. Which dataset(s) the technique is applied on?
1: user feedback gathered from the issue tracking system of the Apache OpenOffice 2: 575 user feedback messages from the feedback gathering system of SEnerCON
7. Is/Are the dataset(s) publicly available online? If yes, please indicate their name and links.
http://se.fbk.eu/technologies/speech-acts-based-analysis
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?
to successfully classify messages into Feature/Enhancement and Other
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, f1
12. Write down any other comments/notes here.
-