How can i improve my app? Classifying user reviews for software maintenance and evolution

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)?

Supervised machine learning with Naive Bayes and BoW features.

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?

Building a classifier that leverages (also) sentiment labels to distinguish between feature request, problem discovery, information seeking, and information giving in app reviews

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

They want to distinguish between feature request (positive sentiment) vs. bug report (negative sentiment)

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

App reviews

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, they train their own classifier using Naive Bayes + BoW using a gold standard of a set of 2090 App Store and Google Play review sentences, i.e. a subset of sentences from the target dataset used for the final classification task.

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?

Not for sentiment analysis

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?

NA

12. Write down any other comments/notes here.

Naive Bayes using Weka on SE-specific gold standard