Security and Emotion: Sentiment Analysis of Security Discussions on GitHub

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)?

Python NLTK Demos and Natural Language Text Processing APIs, http://text-processing.com/,

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.

NLTK http://text-processing.com/

4. What is the main goal of the whole study?

Sentiment Analysis of Security Discussions on GitHub

5. What the researchers want to achieve by applying the technique(s) (e.g., calculate the sentiment polarity of app reviews)?

The researchers wanted to understand whether there was a difference in sentiment of security related discussions and other discussions

6. Which dataset(s) the technique is applied on?

MSR 2014 Mining Challenge Dataset

7. Is/Are the dataset(s) publicly available online? If yes, please indicate their name and links.

Yes https://ghtorrent.org/msr14.html

8. Is the application context (dataset or application domain) different from that for which the technique was originally designed?

Yes

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 comparing the NLTK scores with the manual annotations on a small stratified sample of 30 discussions (stratification based on their security scores). The authors "observed a mixture of agreement and disagreement between the emotion labels computed by NLTK and the ones resulted from manual review." "However, even in cases of disagreement, we noted that the NLTK results were mostly bipolar, having both strong negative and strong positive components."

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?

N/A

12. Write down any other comments/notes here.

It has been replicated later on