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2020 | OriginalPaper | Buchkapitel

30. Analyse der Nützlichkeit von Amazon-Produktbewertungen mittels Text Mining

verfasst von : Florian Bosten, Manuel Di Stefano, Maren Hartmann, Sebastian Sauer, Rüdiger Buchkremer

Erschienen in: Künstliche Intelligenz in Wirtschaft & Gesellschaft

Verlag: Springer Fachmedien Wiesbaden

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Zusammenfassung

Produktrezensionen haben inzwischen einen höheren Stellenwert bekommen als andere Marketingwerkzeuge wie Fragen- und Antwortseiten oder Herstellerforen. In diesem Beitrag wird gezeigt, wie stilistische und semantische Texteigenschaften die vom Leser empfundene Nützlichkeit und sein Abstimmungsverhalten im Hinblick auf dieses Empfinden beeinflussen. Zusätzlich dazu werden in einem separaten Schritt Meta-Informationen, in Form der von Amazon verliehenen Badges, untersucht. Dafür sind nahezu zwei Millionen Amazon-Kundenbewertungen erhoben worden. Dabei kann gezeigt werden, dass sowohl stilistische als auch semantische Eigenschaften und Meta-Informationen zum einen die empfundene Nützlichkeit als auch das Abstimmungsverhalten beeinflussen. Die Untersuchung von unterschiedlichen Produktkategorien zeigt, dass die Beobachtungen grundsätzlicher Natur und nicht auf eine einzelne Sparte oder ein einzelnes Produkt begrenzt sind. Die Ergebnisse zeigen auf, welche Kriterien ein Autor einer Bewertung berücksichtigen muss, um mit seiner Rezension an Reichweite zu gelangen.

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

Nächstes Kapitel Analyse von Nützlichkeits- und Sterne-Online-Bewertungen mittels Machine Learning am Beispiel von Amazon

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Titel: Analyse der Nützlichkeit von Amazon-Produktbewertungen mittels Text Mining
verfasst von: Florian Bosten
Manuel Di Stefano
Maren Hartmann
Sebastian Sauer
Rüdiger Buchkremer
Verlag: Springer Fachmedien Wiesbaden
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_30