A domain knowledge incorporated text mining approach for capturing user needs on BIM applications

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)?

The paper implements a custom made Naive Bayes classifier.

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.

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

Develop an approach that can be used to automatically process user feedback of BIM (Building information modelling) apps.

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

Calculate the sentiment of BIM reviews.

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

Data has been extracted from 759 BIM apps in the Revit app store.

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

Yes, data that has been used can be found here: https://github.com/0AnonymousSite0/A-Domain-Knowledge-Incorporated-Text-Mining-Approach-for-Capturing-User-Needs-on-BIM-Applications/blob/master/Raw%20materials%20and%20Details%20of%20the%20approach%20in%20Appendix%20II.xlsx

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

No, authors designed their app for this approach.

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, authors manually labelled a set of comments, and verified their tool on this set. They do not report any numbers.

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?

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12. Write down any other comments/notes here.

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