Hanne, Thomas

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Hanne
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Thomas
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Hanne, Thomas

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Institut: Institut für Wirtschaftsinformatik × Thema: sentiment analysis ×

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  • Vorschaubild
    Publikation
    Gig Work Business Process Improvement
    (27.08.2018) Pustulka, Elzbieta; Telesko, Rainer; Hanne, Thomas; Wong, Ka Chun [in: 6th International Symposium on Computational and Business Intelligence (ISCBI 2018)]
    We collaborate with a gig work platform company (GPC) in Switzerland. The project aims to improve the business by influencing process management within the GPC, providing automated matching of jobs to workers, improving worker acquisition and worker commitment, and particularly focusing on the prevention of no shows. One expects to achieve financial, organizational and efficiency gains. As research tools we use a combination of text mining and sentiment analysis, Business Process Modeling and Notation (BPMN), interviews with workers and employers, and the design of sociotechnical improvements to the process, including platform improvements and prototypes. Here, we focus on the successful combination of BPMN modelling with sentiment analysis in the identification of problems and generation of ideas for future modifications to the business processes.
    04B - Beitrag Konferenzschrift
  • Vorschaubild
    Publikation
    Multilingual Sentiment Analysis for a Swiss Gig
    (27.08.2018) Pustulka, Elzbieta; Hanne, Thomas; Blumer, Eliane; Frieder, Manuel; Wong, Ka Chun [in: 6th International Symposium on Computational and Business Intelligence (ISCBI 2018)]
    We are developing a multilingual sentiment analysis solution for a Swiss human resource company working in the gig sector. To examine the feasibility of using machine learning in this context, we carried out three sentiment assignment experiments. As test data we use 963 hand annotated comments made by workers and their employers. Our baseline, machine learning (ML) on Twitter, had an accuracy of 0.77 with the Matthews correlation coefficient (MCC) of 0.32. A hybrid solution, Semantria from Lexalytics, had an accuracy of 0.8 with MCC of 0.42, while a tenfold cross-validation on the gig data yielded the accuracy of 0.87, F1 score 0.91, and MCC 0.65. Our solution did not require language assignment or stemming and used standard ML software. This shows that with more training data and some feature engineering, an industrial strength solution to this problem should be possible.
    04B - Beitrag Konferenzschrift