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2020 | OriginalPaper | Buchkapitel

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

verfasst von : Nils Horn, Michel Sebastian Erhardt, Manuel Di Stefano, Florian Bosten, Rüdiger Buchkremer

Erschienen in: Künstliche Intelligenz in Wirtschaft & Gesellschaft

Verlag: Springer Fachmedien Wiesbaden

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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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Metadaten
Titel
Vergleichende Analyse der Word-Embedding-Verfahren Word2Vec und GloVe am Beispiel von Kundenbewertungen eines Online-Versandhändlers
verfasst von
Nils Horn
Michel Sebastian Erhardt
Manuel Di Stefano
Florian Bosten
Rüdiger Buchkremer
Copyright-Jahr
2020
Buch
Künstliche Intelligenz in Wirtschaft & Gesellschaft

Print ISBN: 978-3-658-29549-3

Electronic ISBN: 978-3-658-29550-9

Copyright-Jahr: 2020

DOI
https://doi.org/10.1007/978-3-658-29550-9_29

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