SentiCR: A customized sentiment analysis tool for code review interactions
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)?
SentiCR Non-SE specific Afinn F. A˚ . Nielsen, “A new anew: Evaluation of a word list for sentiment analysis in microblogs,” arXiv preprint arXiv:1103.2903, 2011. NLTK M. Hu and B. Liu, “Mining and summarizing customer reviews,” in Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, 2004, pp. SentiStrength M. Thelwall, K. Buckley, G. Paltoglou, D. Cai, and A. Kappas, “Sentiment strength detection in short informal text,” Journal of the American Society for Information Science and Technology, vol. 61, no. 12, pp. 2544–2558, 2010. TextBlob S. Loria, “Textblob: simplified text processing,” Secondary TextBlob: Simplified Text Processing, 2014. USent N. Pappas, G. Katsimpras, and E. Stamatatos, “Distinguishing the popularity between topics: A system for up-to-date opinion retrieval and mining in the web,” in Computational Linguistics and Intelligent Text Processing, 2013, vol. 7817, pp. 197–209. NLTK Vader C. J. Hutto and E. Gilbert, “Vader: A parsimonious rule-based model for sentiment analysis of social media text,” in Eighth International AAAI Conference on Weblogs and Social Media, 2014. Vivekn V. Narayanan, I. Arora, and A. Bhatia, “Fast and accurate sentiment classification using an enhanced naive bayes model,” in International Conference on Intelligent Data Engineering and Automated Learning. Springer, 2013, pp. 194–201.
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.
SentiCR https://github.com/senticr/SentiCR Afinn https://github.com/fnielsen/afinn NLTK https://www.nltk.org/api/nltk.sentiment.html#module-nltk.sentiment SentiStrength http://sentistrength.wlv.ac.uk/ TextBlob https://textblob.readthedocs.io/en/dev/ USent https://github.com/nik0spapp/usent NLTK Vader https://www.nltk.org/api/nltk.sentiment.html#module-nltk.sentiment.vader Vivekn
4. What is the main goal of the whole study?
Introduction to sentiment analysis for code reviews
5. What the researchers want to achieve by applying the technique(s) (e.g., calculate the sentiment polarity of app reviews)?
sentiment analysis for code reviews
6. Which dataset(s) the technique is applied on?
1,600 labeled code review comments
7. Is/Are the dataset(s) publicly available online? If yes, please indicate their name and links.
https://github.com/collab-uniba/Senti4SD/tree/master/Senti4SD_GoldStandard_and_DSM
8. Is the application context (dataset or application domain) different from that for which the technique was originally designed?
No
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, precision, recall, F1. SentiCR outperformed other tools since these tools are not SE-specific. Gradient Boosting Tree (GBT) is the best performing ML algorithm
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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