Feature-based sentiment analysis of codified project knowledge: A dictionary approach
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)?
Project Knowledge Dictionary (PKD)-based approach
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.
None
4. What is the main goal of the whole study?
to develop a Project Knowledge Dictionary (PKD) to present the project management discipline a way to perform content and sentiment analysis of project-based knowledge content and classify project documents
5. What the researchers want to achieve by applying the technique(s) (e.g., calculate the sentiment polarity of app reviews)?
to identify project knowledge categories, classify sentiment of project knowledge categories and project documents
6. Which dataset(s) the technique is applied on?
355 reports from real-world IT projects
7. Is/Are the dataset(s) publicly available online? If yes, please indicate their name and links.
no
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?
the automated results generated by the PKD were compared with those generated by a human analyst Confusion matrix of sentiment classifications, Precision, recall and F1-scores of classifications per project knowledge category
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?
see 9
12. Write down any other comments/notes here.
-