Spoiler Alert: Using Natural Language Processing to Detect Spoilers in Book Reviews
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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Methods
Adam •
Attention Dropout •
BERT •
Dense Connections •
Dropout •
GELU •
Layer Normalization •
Linear Layer •
Linear Warmup With Linear Decay •
LSTM •
Multi-Head Attention •
Residual Connection •
RoBERTa •
Scaled Dot-Product Attention •
Sigmoid Activation •
Softmax •
Tanh Activation •
Weight Decay •
WordPiece
