The film comments data contains a huge amount of mining research value, and text mining analysis of the animated film’s comments can objectively reflect the quality of the animated film presentation and the problems generally expressed by the audience. However, these film comments are often mixed. The existing well-known film reviews websites have not excavated typical reviews on the users film comment text, so neither the audience nor the animation creators can analyze and apply the comments.This paper presents a general framework for mining typical opinions of film comments and uses crawler technology to obtain network review data, extract comment keywords based on the TF-IDF algorithm, and convert comments segmentation into word vectors trained by a neural network through Word2Vec. Then, using certain extraction rules and the K-means algorithm, the typical opinions with the same semantics but different expressions are aggregated together, and the typical opinions of the animation review of “Monkey King: Hero Is Back” are excavated. From the excavated information, we find out the production problems of the animation, so as to provide a certain reference to the creation of animated movies.

电影评论数据包含巨大的挖掘研究价值，对动画电影评论的文本挖掘分析可以客观地反映动画电影演示的质量和观众普遍表达的问题。但是，这些电影评论经常混杂在一起。现有的著名电影评论网站并未对用户的电影评论文本进行典型评论，因此观众和动画创作者都无法对评论进行分析和应用。爬虫技术获取网络评论数据，基于TF-IDF算法提取评论关键字，并将评论分段转换为通过Word2Vec由神经网络训练的词向量。然后，使用某些提取规则和K-means算法，《齐天大圣：英雄归来》被挖掘。从挖掘的信息中，我们可以发现动画的制作问题，从而为动画电影的创作提供一定的参考。