@inproceedings{conf/pkdd/Zehe0HHRJ16,
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  title = {Prediction of Happy Endings in German Novels.},
  url = {http://dblp.uni-trier.de/db/conf/pkdd/dmnlp2016.html#Zehe0HHRJ16},
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@inproceedings{zehe2016prediction,
  abstract = {Identifying plot structure in novels is a valuable step towards automatic processing of literary corpora. We present an approach to classify novels as either having a happy ending or not. To achieve this, we use features based on different sentiment lexica as input for an SVM- classifier, which yields an average F1-score of about 73%.},
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@inproceedings{zehe2016prediction,
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  title = {Prediction of Happy Endings in German Novels},
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  volume = 1646,
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@inproceedings{zehe2016prediction,
  abstract = {Identifying plot structure in novels is a valuable step towards automatic processing of literary corpora. We present an approach to classify novels as either having a happy ending or not. To achieve this, we use features based on different sentiment lexica as input for an SVM- classifier, which yields an average F1-score of about 73\%.},
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@inproceedings{zehe2016prediction,
  abstract = {Identifying plot structure in novels is a valuable step towards automatic processing of literary corpora. We present an approach to classify novels as either having a happy ending or not. To achieve this, we use features based on different sentiment lexica as input for an SVM- classifier, which yields an average F1-score of about 73%.},
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  title = {Prediction of Happy Endings in German Novels},
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@inproceedings{zehe2016prediction,
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  title = {Prediction of Happy Endings in German Novels},
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@inproceedings{zehe2016prediction,
  abstract = {Identifying plot structure in novels is a valuable step towards automatic processing of literary corpora. We present an approach to classify novels as either having a happy ending or not. To achieve this, we use features based on different sentiment lexica as input for an SVM- classifier, which yields an average F1-score of about 73%.},
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@inproceedings{zehe2016prediction,
  abstract = {Identifying plot structure in novels is a valuable step towards automatic processing of literary corpora. We present an approach to classify novels as either having a happy ending or not. To achieve this, we use features based on different sentiment lexica as input for an SVM- classifier, which yields an average F1-score of about 73%.},
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  title = {Prediction of Happy Endings in German Novels},
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  year = 2016
}