An exploratory study of Twitter messages about software applications
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)?
Sentiment analysis, content classification using ML
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
4. What is the main goal of the whole study?
To study the characteristics of tweets related to software applications and to investigate the potential of sentiment analysis and ML for classifying them.
5. What the researchers want to achieve by applying the technique(s) (e.g., calculate the sentiment polarity of app reviews)?
Classify the content of tweets and their sentiment.
6. Which dataset(s) the technique is applied on?
1,000 apps for each of 30 selected applications
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?
Yes
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 contrasting against a manually curated oracle.
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?
Precision, Recall FM
12. Write down any other comments/notes here.
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