2021/01/15 by Buru Chang, Inggeol Lee, Chang, Buru +5
Computer Science · #Computation and Language (cs.CL) #FOS: Computer and information sciences #Sentiment Analysis and Opinion Mining #Spam and Phishing Detection #Topic Modeling
paper · pdf · doi:10.48550/arxiv.2101.05972
openalex publication_date 2021/01/15 · openalex created_date 2021/02/01 · openalex updated_date 2026/07/28
Several machine learning-based spoiler detection models have been proposed recently to protect users from spoilers on review websites. Although dependency relations between context words are important for detecting spoilers, current attention-based spoiler detection models are insufficient for utilizing dependency relations. To address this problem, we propose a new spoiler detection model called SDGNN that is based on syntax-aware graph neural networks. In the experiments on two real-world benchmark datasets, we show that our SDGNN outperforms the existing spoiler detection models.