Zusammenfassung
Trotz des Forschungsbooms im Bereich Worteinbettungen und ihrer Textmininganwendungen der letzten Jahre, konzentriert sich der Großteil der Publikationen ausschließlich auf die englische Sprache. Außerdem ist die Hyperparameterabstimmung ein Prozess, der selten gut dokumentiert (speziell für nicht-englische Texte), jedoch sehr wichtig ist, um hochqualitative Wortwiedergaben zu erhalten. In dieser Arbeit zeigen wir, wie verschiedene Hyperparameterkombinationen Einfluss auf die resultierenden deutschen Wortvektoren haben und wie diese Wortwiedergaben Teil eines komplexeren Modells sein können. Im Einzelnen führen wir als erstes eine intrinsische Bewertung unserer deutschen Worteinbettungen durch, die später in einem vorausschauenden Stimmungsanalysemodell verwendet werden. Letzteres dient nicht nur einer intrinsischen Bewertung der deutschen Worteinbettungen, sondern zeigt außerdem, ob Kundenwünsche nur durch das Einbetten von Dokumenten vorhergesagt werden können.
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Brito, E., Sifa, R., Cvejoski, K., Ojeda, C., Bauckhage, C. (2017). Towards German Word Embeddings: A Use Case with Predictive Sentiment Analysis. In: Haber, P., Lampoltshammer, T., Mayr, M. (eds) Data Science – Analytics and Applications. Springer Vieweg, Wiesbaden. https://doi.org/10.1007/978-3-658-19287-7_8
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