@inproceedings{liu-etal-2019-dens,
    title = "{DENS}: A Dataset for Multi-class Emotion Analysis",
    author = "Liu, Chen  and
      Osama, Muhammad  and
      De Andrade, Anderson",
    editor = "Inui, Kentaro  and
      Jiang, Jing  and
      Ng, Vincent  and
      Wan, Xiaojun",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/add-emnlp-2024-awards/D19-1656/",
    doi = "10.18653/v1/D19-1656",
    pages = "6293--6298",
    abstract = "We introduce a new dataset for multi-class emotion analysis from long-form narratives in English. The Dataset for Emotions of Narrative Sequences (DENS) was collected from both classic literature available on Project Gutenberg and modern online narratives avail- able on Wattpad, annotated using Amazon Mechanical Turk. A number of statistics and baseline benchmarks are provided for the dataset. Of the tested techniques, we find that the fine-tuning of a pre-trained BERT model achieves the best results, with an average micro-F1 score of 60.4{\%}. Our results show that the dataset provides a novel opportunity in emotion analysis that requires moving beyond existing sentence-level techniques."
}