Extracting Customer-Related Information for Need Identification

  • Antonia FelsEmail author
  • Kristof Briele
  • Max Ellerich
  • Robert Schmitt
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 876)


The increasing amounts of customer-generated content regarding a product or service published in Social Media are an important source of information for companies. Especially for product development projects or the design of service offers, the unbiased feedback expressed in so-called product reviews is most valuable. However, for the effective use of product review content, the development of automated text processing tools is essential; manual text processing approaches are very time-consuming and thus compromise the benefits provided from the extracted information. To date, automated text mining tools focus the analysis of customers preferences and emotions articulated within a product review. An automated extraction and analysis of customer-related content has not yet been investigated in detail. Customer-related content refers to information within a review, which does not primarily concern the product, but provide information about the customer himself, his usage behavior, personal environment and habits. This information is most generally expressed in an objective manner by the author (i.e. customer) and provides an authentic starting point for the identification of customer needs. Particularly for innovative product development, the consideration of customer habits and personal environment is highly relevant for the derivation of underlying needs, which can be more important than the knowledge of specific preferences regarding a product. The objective of this research is the development and validation of a text mining process for the extraction of objective content from product reviews. To this end, German reviews from regarding two product categories are collected and firstly annotated manually for validation reference. Thereafter, a text mining process is developed comprising text preparation, transformation, classification and performance evaluation. Three different classifiers are applied for performance comparison.


User-related content Social media analysis Text classification 



This paper results from the research project “Automated extraction of customer needs from reviews for the enhancement of innovation capability” (SCHM1856/82-1) of the Laboratory for Machine Tools and Product Engineering (WZL), RWTH Aachen University, Germany. The research project has been funded by the German National Science Foundation (DFG). The authors would like to express their gratitude to all parties involved.


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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Antonia Fels
    • 1
    Email author
  • Kristof Briele
    • 1
  • Max Ellerich
    • 1
  • Robert Schmitt
    • 1
  1. 1.Laboratory for Machine Tools and Production Engineering WZL of RWTH Aachen UniversityAachenGermany

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