Fine-Grained Spoiler Detection from Large-Scale Review Corpora
Mengting Wan, Rishabh Misra, Ndapa Nakashole, Julian McAuley
Abstract
This paper presents computational approaches for automatically detecting critical plot twists in reviews of media products. First, we created a large-scale book review dataset that includes fine-grained spoiler annotations at the sentence-level, as well as book and (anonymized) user information. Second, we carefully analyzed this dataset, and found that: spoiler language tends to be book-specific; spoiler distributions vary greatly across books and review authors; and spoiler sentences tend to jointly appear in the latter part of reviews. Third, inspired by these findings, we developed an end-to-end neural network architecture to detect spoiler sentences in review corpora. Quantitative and qualitative results demonstrate that the proposed method substantially outperforms existing baselines.- Anthology ID:
- P19-1248
- Volume:
- Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
- Month:
- July
- Year:
- 2019
- Address:
- Florence, Italy
- Editors:
- Anna Korhonen, David Traum, Lluís Màrquez
- Venue:
- ACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 2605–2610
- Language:
- URL:
- https://aclanthology.org/P19-1248
- DOI:
- 10.18653/v1/P19-1248
- Cite (ACL):
- Mengting Wan, Rishabh Misra, Ndapa Nakashole, and Julian McAuley. 2019. Fine-Grained Spoiler Detection from Large-Scale Review Corpora. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 2605–2610, Florence, Italy. Association for Computational Linguistics.
- Cite (Informal):
- Fine-Grained Spoiler Detection from Large-Scale Review Corpora (Wan et al., ACL 2019)
- PDF:
- https://preview.aclanthology.org/teach-a-man-to-fish/P19-1248.pdf