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Vergleichende Analyse der Word-Embedding-Verfahren Word2Vec und GloVe am Beispiel von Kundenbewertungen eines Online-Versandhändlers

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Künstliche Intelligenz in Wirtschaft & Gesellschaft

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Zusammenfassung

Um eine zielgerichtete Aussage zu Inhalten in Texten zu liefern, werden Bedeutungen von Wörtern als Vektoren dargestellt. Zur Vektorisierung, welche auch als „Word-Embedding-Verfahren“ bezeichnet werden, sind bereits existierende Verfahren zu überprüfen, denn die Wahl des Lernalgorithmus hat einen großen Einfluss auf die Genauigkeit des Gesamtanalyseergebnisses bei einer bestimmten Kategorie von Texten. Es wird beschrieben, wie die beiden populären Verfahren „Word2Vec“ und „GloVe“ für die Analyse von Online-Bewertungen konzipiert, implementiert und angewendet werden können. Eine quantitative Evaluation der Ergebnisse erfolgt auf Basis von Rezessionen des Versandhändlers Amazon.com. In einer abschließenden Diskussion sollen die Testergebnisse validiert und die Grenzen aufgezeigt werden.

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  1. FOM Hochschule, Düsseldorf, Deutschland

    Nils Horn, Michel Sebastian Erhardt, Manuel Di Stefano, Florian Bosten & Rüdiger Buchkremer

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    Rüdiger Buchkremer

  2. FOM Hochschule für Oekonomie & Management, Essen, Germany

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  3. FOM Hochschule für Oekonomie & Management, Frankfurt a. M., Germany

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Horn, N., Erhardt, M.S., Di Stefano, M., Bosten, F., Buchkremer, R. (2020). Vergleichende Analyse der Word-Embedding-Verfahren Word2Vec und GloVe am Beispiel von Kundenbewertungen eines Online-Versandhändlers. In: Buchkremer, R., Heupel, T., Koch, O. (eds) Künstliche Intelligenz in Wirtschaft & Gesellschaft. FOM-Edition. Springer Gabler, Wiesbaden. https://doi.org/10.1007/978-3-658-29550-9_29

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