Details
| Original language | English |
|---|---|
| Title of host publication | Product-Focused Software Process Improvement |
| Editors | Davide Taibi, Marco Kuhrmann, Tommi Mikkonen, Pekka Abrahamsson, Jil Klünder |
| Place of Publication | Cham |
| Publisher | Springer International Publishing AG |
| Pages | 108-123 |
| Number of pages | 16 |
| ISBN (electronic) | 978-3-031-21388-5 |
| ISBN (print) | 978-3-031-21387-8 |
| Publication status | Published - 14 Nov 2022 |
Publication series
| Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
|---|---|
| Volume | 13709 LNCS |
| ISSN (Print) | 0302-9743 |
| ISSN (electronic) | 1611-3349 |
Abstract
Keywords
- Cross-platform setting, Development team, Machine learning, Majority voting, Sentiment analysis
ASJC Scopus subject areas
- Mathematics(all)
- Theoretical Computer Science
- Computer Science(all)
- General Computer Science
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Product-Focused Software Process Improvement. ed. / Davide Taibi; Marco Kuhrmann; Tommi Mikkonen; Pekka Abrahamsson; Jil Klünder. Cham: Springer International Publishing AG, 2022. p. 108-123 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 13709 LNCS).
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
TY - GEN
T1 - On the Limitations of Combining Sentiment Analysis Tools in a Cross-Platform Setting
AU - Obaidi, Martin
AU - Holm, Henrik
AU - Schneider, Kurt
AU - Klünder, Jil
N1 - Funding Information: This research was funded by the Leibniz University Hannover as a Leibniz Young Investigator Grant (Project ComContA, Project Number 85430128, 2020–2022).
PY - 2022/11/14
Y1 - 2022/11/14
N2 - A positive working climate is essential in modern software development. It enhances productivity since a satisfied developer tends to deliver better results. Sentiment analysis tools are a means to analyze and classify textual communication between developers according to the polarity of the statements. Most of these tools deliver promising results when used with test data from the domain they are developed for (e.g., GitHub). But the tools' outcomes lack reliability when used in a different domain (e.g., Stack Overflow). One possible way to mitigate this problem is to combine different tools trained in different domains. In this paper, we analyze a combination of three sentiment analysis tools in a voting classifier according to their reliability and performance. The tools are trained and evaluated using five already existing polarity data sets (e.g. from GitHub). The results indicate that this kind of combination of tools is a good choice in the within-platform setting. However, a majority vote does not necessarily lead to better results when applying in cross-platform domains. In most cases, the best individual tool in the ensemble is preferable. This is mainly due to the often large difference in performance of the individual tools, even on the same data set. However, this may also be due to the different annotated data sets.
AB - A positive working climate is essential in modern software development. It enhances productivity since a satisfied developer tends to deliver better results. Sentiment analysis tools are a means to analyze and classify textual communication between developers according to the polarity of the statements. Most of these tools deliver promising results when used with test data from the domain they are developed for (e.g., GitHub). But the tools' outcomes lack reliability when used in a different domain (e.g., Stack Overflow). One possible way to mitigate this problem is to combine different tools trained in different domains. In this paper, we analyze a combination of three sentiment analysis tools in a voting classifier according to their reliability and performance. The tools are trained and evaluated using five already existing polarity data sets (e.g. from GitHub). The results indicate that this kind of combination of tools is a good choice in the within-platform setting. However, a majority vote does not necessarily lead to better results when applying in cross-platform domains. In most cases, the best individual tool in the ensemble is preferable. This is mainly due to the often large difference in performance of the individual tools, even on the same data set. However, this may also be due to the different annotated data sets.
KW - Cross-platform setting
KW - Development team
KW - Machine learning
KW - Majority voting
KW - Sentiment analysis
UR - http://www.scopus.com/inward/record.url?scp=85142722433&partnerID=8YFLogxK
U2 - 10.1007/978-3-031-21388-5_8
DO - 10.1007/978-3-031-21388-5_8
M3 - Conference contribution
SN - 978-3-031-21387-8
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 108
EP - 123
BT - Product-Focused Software Process Improvement
A2 - Taibi, Davide
A2 - Kuhrmann, Marco
A2 - Mikkonen, Tommi
A2 - Abrahamsson, Pekka
A2 - Klünder, Jil
PB - Springer International Publishing AG
CY - Cham
ER -