Sentiment Classification Using N-Gram Inverse Document Frequency and Automated Machine Learning
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)?
SentiStrength, NLTK, StanfordCoreNlp, SentiStrength-SE and the methods proposed in the paper.
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.
SentiStrength: http://sentistrength.wlv.ac.uk/ SentiStrength-SE: https://www.sciencedirect.com/science/article/pii/S0164121218301675 StanfordCoreNLP: https://stanfordnlp.github.io/CoreNLP/ NLTK: https://www.nltk.org/
4. What is the main goal of the whole study?
Develop a sentiment analysis tool using n-grams and their frequency as features.
5. What the researchers want to achieve by applying the technique(s) (e.g., calculate the sentiment polarity of app reviews)?
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6. Which dataset(s) the technique is applied on?
A dataset combining JIRA issues, SO data and APP reviews.
7. Is/Are the dataset(s) publicly available online? If yes, please indicate their name and links.
B. Lin, F. Zampetti, G. Bavota, M. Di Penta, M. Lanza, and R. Oliveto, “Sentiment analysis for software engineering: How far can we go?” in Proc. 2018 IEEE/ACM 40th Int. Conf. Software Engineering (ICSE), pp. 94–104.
8. Is the application context (dataset or application domain) different from that for which the technique was originally designed?
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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 and F1 measure are verified and compared to all techniques, this paints quite a complex picture, as per category and per sentiment level (pos/neg/neut) different tools excel. However, the tool N-grams with auto learn (proposed by the authors) appears to be quite effective in detecting sentences with negative sentiment.
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?
no
12. Write down any other comments/notes here.
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