  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.
