A Method to Transform Automatically Extracted Product Features into Inputs for Kano-Like Models

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)?

Naïve Bayes, MaxEnt, Decision Trees, Support Vector Machines (SVM) a dictionary-based method

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 automatically transform feature related text extracted from online open sources into inputs for Kano-like models. The Kano model defines five categories (O, A, M, I, R): One-dimensional Quality, A = Attractive Quality, M = Must-be Quality, I = Indifferent Quality, R = Reverse Quality

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

ML approach to decide whether a text line corresponds to an answer of the functional or dysfunctional question asked in the Kano method. a dictionary-based method to classify the sentiment of each statement.

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

text from Stack Overflow, and from an App store (both Google Play and Apple Store)

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?

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?

randomly chose 20% of the samples and manually check the results confusion matrix for functional/dysfunctional classification (overall accuracy 75%) for the correctly classified sentences and a new dataset: accuracy rate for each category of sentiment (overall, 71.6-81%)

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.

-