Automatic Identification of Assumptions from the Hibernate Developer Mailing List
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)?
Perceptron (Pct), Logistic Regression (LR), Linear Discriminant Analysis (LDA), K-Nearest Neighbors (KNN), Classification And Regression Tree (CART), Naive Bayes (NB), and Support Vector Machines (SVM)
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.
all common algorithms
4. What is the main goal of the whole study?
to evaluate the performance of existing machine learning classification algorithms for automatic assumption identification
5. What the researchers want to achieve by applying the technique(s) (e.g., calculate the sentiment polarity of app reviews)?
classify sentences as assumption and non-assumption
6. Which dataset(s) the technique is applied on?
a dataset extracted from the Hibernate developer mailing list, including 400 “Assumption” sentences and 400 “Non-Assumption” sentences
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, classifiers are 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?
precision, recall, f1-value, ROC curves, AUC values SVM algorithm achieved the best performance (with a precision of 0.829, a recall of 0.812, and an F1-score of 0.819)
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.
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