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End date: 2021 × Start date: 2021 × DDC: 020 × Version: acceptedVersion × WGL Institute: TIB ×

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    Roadmap to FAIR Research Information in Open Infrastructures
    (Abingdon : Routledge, 2021) Hauschke, Christian; Nazarovets, Serhii; Altemeier, Franziska; Kaliuzhna, Nataliia
    The FAIR Principles were designed to improve the findability, accessibility, interoperability and reusability of data holdings by humans and machines. The principles can be applied to research information too. We present the results of the discussions that took place during the series of online workshops with experts on Research Information and FAIR Guiding Principles. We provide high-level criteria on how to foster findable, accessible, interoperable and reusable, and we hope that our roadmap for FAIR research information in open infrastructures bring many benefits to a diverse group of stakeholders of the scientific ecosystem.
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    A Fair and Comprehensive Comparison of Multimodal Tweet Sentiment Analysis Methods
    (Ithaka : Cornell University, 2021) Cheema, Gullal S.; Hakimov, Sherzod; Müller-Budack, Eric; Ewerth, Ralph
    Opinion and sentiment analysis is a vital task to characterize subjective information in social media posts. In this paper, we present a comprehensive experimental evaluation and comparison with six state-of-the-art methods, from which we have re-implemented one of them. In addition, we investigate different textual and visual feature embeddings that cover different aspects of the content, as well as the recently introduced multimodal CLIP embeddings. Experimental results are presented for two different publicly available benchmark datasets of tweets and corresponding images. In contrast to the evaluation methodology of previous work, we introduce a reproducible and fair evaluation scheme to make results comparable. Finally, we conduct an error analysis to outline the limitations of the methods and possibilities for the future work.