Constructing Dictionary to Analyze Features Sentiment of a Movie Based on Danmakus

Abstract

As a new commenting mode, danmaku not only shows the subjective attitude or emotion of the reviewer, but also has instantaneity and interactivity compared with traditional comments. In order to improve the existing film evaluation mechanism of mainstream film rating websites, this paper trains the word vector model based on movies’ danmaku and builds the movie feature word lexicon iteratively. And then, through the Boson (https://bosonnlp.com/dev/resource) sentiment dictionary and TF-IDF algorithm, we set up the feature-sentiment dictionary. Finally, we use the feature-sentiment dictionary and combine the dictionary of the degree words to calculate the sentiment score of each feature based on the movie danmaku. Our experimental results are compared with the scores of a film rating website “Mtime” (http://www.mtime.com/). The comparison proves that our method of analyzing and computing sentiment of movie features is not only novel but also effective.