GUSD claims state-of-the-art spoiler detection on IMDb datasets using genre-aware routing and user-bias features from dynamic graph pretraining.
Spoiler Alert: Using Natural Language Processing to Detect Spoilers in Book Reviews
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abstract
This paper presents an NLP (Natural Language Processing) approach to detecting spoilers in book reviews, using the University of California San Diego (UCSD) Goodreads Spoiler dataset. We explored the use of LSTM, BERT, and RoBERTa language models to perform spoiler detection at the sentence-level. This was contrasted with a UCSD paper which performed the same task, but using handcrafted features in its data preparation. Despite eschewing the use of handcrafted features, our results from the LSTM model were able to slightly exceed the UCSD team's performance in spoiler detection.
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Unveiling the Hidden: Movie Genre and User Bias in Spoiler Detection
GUSD claims state-of-the-art spoiler detection on IMDb datasets using genre-aware routing and user-bias features from dynamic graph pretraining.