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