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On the Limitations of Combining Sentiment Analysis Tools in a Cross-Platform Setting

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Product-Focused Software Process Improvement (PROFES 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13709))

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Abstract

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.

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Acknowledgment

This research was funded by the Leibniz University Hannover as a Leibniz Young Investigator Grant (Project ComContA, Project Number 85430128, 2020–2022).

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Authors and Affiliations

  1. Software Engineering Group, Leibniz University Hannover, Welfengarten 1, 30167, Hannover, Germany

    Martin Obaidi, Henrik Holm, Kurt Schneider & Jil Klünder

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  1. Martin Obaidi
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  2. Henrik Holm
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  3. Kurt Schneider
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  4. Jil Klünder
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Corresponding author

Correspondence to Martin Obaidi .

Editor information

Editors and Affiliations

  1. Tampere University, Tampere, Finland

    Davide Taibi

  2. Reutlingen University, Reutlingen, Germany

    Marco Kuhrmann

  3. University of Jyväskylä, Jyväskylä, Finland

    Tommi Mikkonen

  4. Leibniz University Hannover, Hannover, Germany

    Jil Klünder

  5. University of Jyväskylä, Jyväskylä, Finland

    Pekka Abrahamsson

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Obaidi, M., Holm, H., Schneider, K., Klünder, J. (2022). On the Limitations of Combining Sentiment Analysis Tools in a Cross-Platform Setting. In: Taibi, D., Kuhrmann, M., Mikkonen, T., Klünder, J., Abrahamsson, P. (eds) Product-Focused Software Process Improvement. PROFES 2022. Lecture Notes in Computer Science, vol 13709. Springer, Cham. https://doi.org/10.1007/978-3-031-21388-5_8

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  • DOI: https://doi.org/10.1007/978-3-031-21388-5_8

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  • Publisher Name: Springer, Cham

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  • Online ISBN: 978-3-031-21388-5

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