Sentiment Analysis Based Requirement Evolution Prediction

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)?

Long Short-term Memory (LSTM) with the Hierarchical Latent Dirichlet Allocation (HLDA)

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.

No

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

to analyze user reviews automatically for product feature requirements evolution prediction

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

LSTM-based RNN framework for the sentence-level sentiment classification task HLDA is to show a detailed overview of users’ primary concerns with the information about which specific product features are positive or negative

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

752,937 Android review dataset from Amazon review datasets

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

only Amazon review datasets http://jmcauley.ucsd. edu/data/amazon/

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

retrained

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?

sentiment classification: approach compared with the ASUM topic modeling: created a groundtruth set of product features referred to in the reviews and their associated sentiments

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?

sentiment classification: Area Under the Curve (AUC) requirements relevance and feature-dependent sentiment classification: precision, recall, and the F-measure

12. Write down any other comments/notes here.

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