EmoTxt: a toolkit for emotion recognition from text
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)?
The authors present EmoTxT, a tool for emotion recognition in text
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.
EmoTxT: https://github.com/collab-uniba/Emotion_and_Polarity_SO
4. What is the main goal of the whole study?
To present EmoTxT, a tool for the recognition of emotions in online text.
5. What the researchers want to achieve by applying the technique(s) (e.g., calculate the sentiment polarity of app reviews)?
Extract emotions from text.
6. Which dataset(s) the technique is applied on?
4,800 Stack Overflow posts annotated by 12 raters The Jira dataset by Ortu et al., composed by 4,000 comments on the Jira issue tracker
7. Is/Are the dataset(s) publicly available online? If yes, please indicate their name and links.
Stack Overflow dataset: https://github.com/collab-uniba/Emotion_and_Polarity_SO/tree/master/java/DatasetSO/StackOverflowCSV Jira: Should be but I didn't find an URL
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, by using an annotated dataset to train (70%) and test (30%) the approach. Precision, Recall, and F1 are > 70% for all sentiments but "Surprise", where they are 58%
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?
Recall, Precision, F1
12. Write down any other comments/notes here.
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