citation: @inproceedings{dataset_de_sb10k,
      title = "A {T}witter Corpus and Benchmark Resources for {G}erman Sentiment Analysis",
      author = "Cieliebak, Mark  and
          Deriu, Jan Milan  and
          Egger, Dominic  and
          Uzdilli, Fatih",
      booktitle = "Proceedings of the Fifth International Workshop on Natural Language Processing for Social Media",
      month = apr,
      year = "2017",
      address = "Valencia, Spain",
      publisher = "Association for Computational Linguistics",
      url = "https://aclanthology.org/W17-1106",
      doi = "10.18653/v1/W17-1106",
      pages = "45--51",
      abstract = "In this paper we present SB10k, a new corpus for sentiment analysis with approx. 10,000 German tweets. We use this new corpus and two existing corpora to provide state-of-the-art benchmarks for sentiment analysis in German: we implemented a CNN (based on the winning system of SemEval-2016) and a feature-based SVM and compare their performance on all three corpora. For the CNN, we also created German word embeddings trained on 300M tweets. These word embeddings were then optimized for sentiment analysis using distant-supervised learning. The new corpus, the German word embeddings (plain and optimized), and source code to re-run the benchmarks are publicly available.",
  }
  