DENS: A Dataset for Multi-class Emotion Analysis

Chen Liu, Muhammad Osama, Anderson De Andrade


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.
Anthology ID:
D19-1656
Volume:
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:
November
Year:
2019
Address:
Hong Kong, China
Editors:
Kentaro Inui, Jing Jiang, Vincent Ng, Xiaojun Wan
Venues:
EMNLP | IJCNLP
SIG:
SIGDAT
Publisher:
Association for Computational Linguistics
Note:
Pages:
6293–6298
Language:
URL:
https://aclanthology.org/D19-1656
DOI:
10.18653/v1/D19-1656
Bibkey:
Cite (ACL):
Chen Liu, Muhammad Osama, and Anderson De Andrade. 2019. DENS: A Dataset for Multi-class Emotion Analysis. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pages 6293–6298, Hong Kong, China. Association for Computational Linguistics.
Cite (Informal):
DENS: A Dataset for Multi-class Emotion Analysis (Liu et al., EMNLP-IJCNLP 2019)
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https://aclanthology.org/D19-1656.pdf
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