Understanding customer satisfaction via deep learning and natural language processing

Ángeles Aldunate, Sebastián Maldonado, Carla Vairetti*, Guillermo Armelini

*Autor correspondiente de este trabajo

Producción científica: Contribución a una revistaArtículorevisión exhaustiva

26 Citas (Scopus)


t is of utmost importance for marketing academics and service industry practitioners to understand the factors that influence customer satisfaction. This study proposes a novel framework to analyze open-ended survey data and extract drivers of customer satisfaction. This is done automatically via deep learning models for natural language processing. According to 11 drivers acknowledged by the marketing literature to determine customer experience, the data is cast into a multi-label classification problem. This expert system not only supports the automatic analysis of new data but also ranks the drivers according to their importance to various service industries and provides important insights into their applications. Experiments carried out using 25,943 customer survey responses related to 39 service companies in 13 different economic sectors show that the drivers can be identified accurately.
Idioma originalInglés
Número de artículo118309
PublicaciónExpert Systems with Applications
EstadoPublicada - 15 dic. 2022

Nota bibliográfica

Publisher Copyright:
© 2022 Elsevier Ltd

Palabras clave

  • Analytics
  • Customer satisfaction
  • Customer feedback
  • Natural language processing
  • Deep learning
  • BERT


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