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Entscheidungsunterstützung im Online-Handel

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Big Data Analytics

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Zusammenfassung

Kundenfeedback im Online-Handel in Form von Produktrezensionen liefern wichtige Informationen über die Kundenwahrnehmung von Produkten. So beschreiben sie verwendete Materialien, Farben, die Passform, das Design und den Anwendungszweck eines Produkts. Das Kundenfeedback liegt hier in unstrukturierter Textform vor, weshalb zur Verarbeitung Ansätze aus dem Gebiet des Natural Language Processing und des maschinellen Lernens von Vorteil sind. In diesem Beitrag wird ein hybrider Ansatz zur Kategorisierung von Produktrezensionen vorgestellt, der die Vorteile des maschinellen Lernens des Word2Vec-Algorithmus und die der menschlichen Expertise vereint. Das daraus resultierende Datenmodell wird im Anschluss anhand einer Praxisanwendung zum Thema Produktempfehlungen demonstriert.

Überarbeiteter Beitrag basierend auf Götz R, Piatta A, Bodendorf F (2019) Hybrider Ansatz zur automatisierten Themen-Klassifizierung von Produktrezensionen, HMD – Praxis der Wirtschaftsinformatik Heft 329, 56: 932–946.

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Authors and Affiliations

  1. Institut für Wirtschaftsinformatik, Friedrich-Alexander-Universität Erlangen-Nürnberg, Nürnberg, Deutschland

    René Götz, Alexander Piazza & Freimut Bodendorf

Authors
  1. René Götz
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  2. Alexander Piazza
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  3. Freimut Bodendorf
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Corresponding author

Correspondence to René Götz .

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Editors and Affiliations

  1. IT Business Integration, Genossenschaft Migros Zürich, Zürich, Switzerland

    Sara D'Onofrio

  2. Universität Fribourg, Fribourg, Switzerland

    Andreas Meier

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Götz, R., Piazza, A., Bodendorf, F. (2021). Entscheidungsunterstützung im Online-Handel. In: D'Onofrio, S., Meier, A. (eds) Big Data Analytics. Edition HMD. Springer Vieweg, Wiesbaden. https://doi.org/10.1007/978-3-658-32236-6_5

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