Spoiler Detection from Review Comments using Story Documents
Kyosuke Maeda, Yoshinori Hijikata, Satoshi Nakamura, Nobuchika Sakata
Users' review comments in shopping sites are useful for other users to decide whether or not buy the item. While users' comments or opinions are included in the reviews, descriptions about story contents are sometimes included in the reviews toward items with story like novels or movies. In some cases, these descriptions may spoil reader's or viewer's enjoyment and excitement. Hereinafter, we call these descriptions spoilers. Spoilers might be related to the position in the story line. In this study we use story documents that record all of the details of the given story. Using the story documents, we investigate the location to which the content of the spoilers correspond in the story documents. Based on the result of the investigation, we develop a method for detecting spoilers in users' review comments. We compared our proposed method with some baselines that detect spoilers using machine learning techniques with bag of words model. We found that our method performs as well as the baseline. This means that we can detect spoilers in the same level of precision even if we do not have labeled data on spoilers.