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
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
Bibkey:
Copy Citation:
PDF:
https://aclanthology.org/P19-1248.pdf