Published on Fri May 31 2019

Fine-Grained Spoiler Detection from Large-Scale Review Corpora

Mengting Wan, Rishabh Misra, Ndapa Nakashole, Julian McAuley

This paper presents computational approaches for automatically detecting plot twists in reviews of media products. We created a large-scale book review dataset that includes fine-grained spoiler annotations.

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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.

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