DENS: A dataset for multi-class emotion analysis

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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.

Citation

Chen Liu, Osama Muhammad, & Anderson de Andrade. (2019). “DENS: A dataset for multi-class emotion analysis.” EMNLP.

@inproceedings{DBLP:conf/emnlp/LiuOA19,
  author       = {Chen Liu and
                  Muhammad Osama and
                  Anderson de Andrade},
  editor       = {Kentaro Inui and
                  Jing Jiang and
                  Vincent Ng and
                  Xiaojun Wan},
  title        = {{DENS:} {A} Dataset for Multi-class Emotion Analysis},
  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} 2019, Hong Kong, China,
                  November 3-7, 2019},
  pages        = {6292--6297},
  publisher    = {Association for Computational Linguistics},
  year         = {2019},
  url          = {https://doi.org/10.18653/v1/D19-1656},
  doi          = {10.18653/V1/D19-1656},
  timestamp    = {Tue, 22 Aug 2023 20:03:10 +0200},
  biburl       = {https://dblp.org/rec/conf/emnlp/LiuOA19.bib},
  bibsource    = {dblp computer science bibliography, https://dblp.org}
}