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A Review and Cluster Analysis of German Polarity Resources for Sentiment Analysis

Authors Bettina M. J. Kern , Andreas Baumann , Thomas E. Kolb , Katharina Sekanina, Klaus Hofmann, Tanja Wissik , Julia Neidhardt



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Author Details

Bettina M. J. Kern
  • University of Vienna, Austria
Andreas Baumann
  • University of Vienna, Austria
Thomas E. Kolb
  • TU Wien, Austria
Katharina Sekanina
  • University of Vienna, Austria
Klaus Hofmann
  • University of Vienna, Austria
Tanja Wissik
  • Austrian Academy of Sciences, Vienna, Austria
Julia Neidhardt
  • TU Wien, Austria

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Bettina M. J. Kern, Andreas Baumann, Thomas E. Kolb, Katharina Sekanina, Klaus Hofmann, Tanja Wissik, and Julia Neidhardt. A Review and Cluster Analysis of German Polarity Resources for Sentiment Analysis. In 3rd Conference on Language, Data and Knowledge (LDK 2021). Open Access Series in Informatics (OASIcs), Volume 93, pp. 37:1-37:17, Schloss Dagstuhl - Leibniz-Zentrum für Informatik (2021)
https://doi.org/10.4230/OASIcs.LDK.2021.37

Abstract

The domain of German polarity dictionaries is heterogeneous with many small dictionaries created for different purposes and using different methods. This paper aims to map out the landscape of freely available German polarity dictionaries by clustering them to uncover similarities and shared features. We find that, although most dictionaries seem to agree in their assessment of a word’s sentiment, subsets of them form groups of interrelated dictionaries. These dependencies are in most cases an immediate reflex of how these dictionaries were designed and compiled. As a consequence, we argue that sentiment evaluation should be based on multiple and diverse sentiment resources in order to avoid error propagation and amplification of potential biases.

Subject Classification

ACM Subject Classification
  • Computing methodologies → Cluster analysis
Keywords
  • cluster analysis
  • sentiment polarity
  • sentiment analysis
  • German
  • review

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