Many videogames suffer "review bombing" -a large volume of unusually low
scores that in many cases do not reflect the real quality of the product- when
rated by users. By taking Metacritic's 50,000+ user score aggregations for PC
games in English language, we use a Natural Language Processing (NLP) approach
to try to understand the main words and concepts appearing in such cases,
reaching a 0.88 accuracy on a validation set when distinguishing between just
bad ratings and review bombings. By uncovering and analyzing the patterns
driving this phenomenon, these results could be used to further mitigate these
situations.

对于许多被用户打分的电子游戏来说，很多时候都会出现 “点评攻击” 现象，即大量低分评价，这些评价往往不能真实反映产品的质量。通过使用英语 PC 游戏的 Metacritic 上的 5 万多用户评分汇总，并采用自然语言处理（NLP）方法，本文试图了解这些情况中出现的主要词语和概念，当区分仅仅是差评和点评攻击时，在验证集上达到了 0.88 的准确率。通过揭示和分析推动这一现象的模式，这些结果可以用于进一步减轻这些情况。