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GermanPolarityClues: A Lexical Resource for German Sentiment Analysis

In this paper, we propose GermanPolarityClues, a new publicly available lexical resource for sentiment analysis for the German language. While sentiment analysis and polarity classification has been extensively studied at different document levels (e.g. sentences and phrases), only a few approaches explored the effect of a polarity-based feature selection and subjectivity resources for the German language. This paper evaluates four different English and three different German sentiment resources in a comparative manner by combining a polarity-based feature selection with SVM-based machine learning classifier. Using a semi-automatic translation approach, we were able to construct three different resources for a German sentiment analysis. The manually finalized GermanPolarityClues dictionary offers thereby a number of 10, 141 polarity features, associated to three numerical polarity scores, determining the positive, negative and neutral direction of specific term features. While the results show that the size of dictionaries clearly correlate to polarity-based feature coverage, this property does not correlate to classification accuracy. Using a polarity-based feature selection, considering a minimum amount of prior polarity features, in combination with SVM-based machine learning methods exhibits for both languages the best performance (F1: 0.83-0.88).