Abstract
Sentiment Analysis is a Natural Language Processing-task that is relevant in a number of contexts, including the analysis of literature. We report on ongoing research towards enabling, for the first time, sentence-level Sentiment Analysis in the domain of German novels. We create a labelled dataset from sentences extracted from German novels and, by adapting existing sentiment classifiers, reach promising F1-scores of 0.67 for binary polarity classification.
Und sie lebten glücklich bis ans Ende ihrer Tage. (German fairy tales)
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Notes
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Ternary labels are transformed into binary labels by omission of the neutral class (0).
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We use the words “nicht” (not), “kein” (no), “ohne” (without), “nie” (never), “niemals” (never), “nirgends” (nowhere), “niemand” (nobody), and “keiner”(nobody) as negation markers.
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We also evaluated other selection schemes, but found that random selection yielded too many unemotional sentences, while \(r=e\) preferred very long ones.
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Available on http://dmir.org/senticrowd/senticrowd. Login is possible with both “Microworkers-ID” and “Kampagnen-ID” set to “demo” in the upper form.
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Zehe, A., Becker, M., Jannidis, F., Hotho, A. (2017). Towards Sentiment Analysis on German Literature. In: Kern-Isberner, G., Fürnkranz, J., Thimm, M. (eds) KI 2017: Advances in Artificial Intelligence. KI 2017. Lecture Notes in Computer Science(), vol 10505. Springer, Cham. https://doi.org/10.1007/978-3-319-67190-1_36
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