Winning of Hearts and Minds: Integrating Sentiment Analytics into the Analysis of Contradictions
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)?
They customised two popular sentiment analysis dictionaries – ‘Opinion Lexicon’ and ‘Comparative Words’. To analyse the sentiment in the message body content, the message body is split into tokens and using a rule-based algorithm in combination with two dictionaries, assigned a positive, neutral, or negative score. The assigned sentiment scores ranged from ‘Strong negative’ (−20), Weak negative (−10), Neutral (0), Positive (+10), and Strong positive (+20). A token is assigned a score according to the matching word found in the dictionaries and the overall sentiment of a message was computed as the sum of all scores assigned to the tokens contained in that message. The research method consists of three inter-related phases, namely, (i) data extraction, (ii) data preprocessing, and (iii) data analysis.
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.
None, they rather use existing lexicons. References are not provided by the authors so I don't know which resources they actually used. - Opinion Lexicon: https://www.cs.uic.edu/~lzhang3/programs/OpinionLexicon.html (based on my own Google search, not sure this is the resource used in the paper) - Comparative Words (link not available)
4. What is the main goal of the whole study?
OSS projects are notoriously subject to contradictions (i.e. tensions, conflict, breakdown in communication). Interactions in open source communities are often informal, and enacted through online discussion forums. While discussion and associated sentiment is critical to sustaining open source communities, they have not been studied to date. To address this gap in knowledge, this study uses sentiment analytics to illuminate the frequency of 2,364 discursive manifestations of contradictions through the theoretical lens of Activity Theory (AT). The study contributes to current discourse on contradictions by demonstrating the importance of dialectical contradictions as a driving force for learning, change, and sustaining open source communities.
5. What the researchers want to achieve by applying the technique(s) (e.g., calculate the sentiment polarity of app reviews)?
Calculate the sentiment polarity of discussions to identify breakdowns in communication.
6. Which dataset(s) the technique is applied on?
Comprised of extracting messages from the dpdk-dev mailing list archived at http://mails.dpdk.org/archives/dev/. A total of 13,461 messages were extracted in RAR file format.
7. Is/Are the dataset(s) publicly available online? If yes, please indicate their name and links.
NA
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?
no
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?
NA
12. Write down any other comments/notes here.
-