Development emails content analyzer: Intention mining in developer 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)?
Natural language processing to apply pattern matching on sentences
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.
Stanford typed dependencies to identify grammatical relationships between words. de Marneffe et al. Generating typed dependency parses from phrase structure parses.
4. What is the main goal of the whole study?
To classify the intention of different sentences in developers' emails.
5. What the researchers want to achieve by applying the technique(s) (e.g., calculate the sentiment polarity of app reviews)?
Identify recurring patterns in emails that can help in classifying a sentence in a specific category (e.g., feature request).
6. Which dataset(s) the technique is applied on?
Emails from two open source projects, 302 from Qt, and 100 from Ubuntu.
7. Is/Are the dataset(s) publicly available online? If yes, please indicate their name and links.
It was, link is broken.
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, they run the approach on a set of previously unseen emails on which the authors built an oracle with labeled sentences. Then, they measure precision/recall/FM< of the classification.
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, FM
12. Write down any other comments/notes here.
-