基于弹幕情感分析的视频片段推荐模型

摘要

针对传统的视频情感分析方法计算效率较低且结果不易解释等问题,提出一种基于弹幕文本的视频片段情感识别算法,并以此作为视频片段的推荐依据.首先对基于情感分析的视频片段推荐问题提出形式化描述.其次,通过构建基于隐含狄利克雷分布(LDA)的弹幕词语分类,评估弹幕词语在视频片段中的多维情感向量,同时,根据视频片段之间的情感依赖关系推荐视频的情感片段.所提方法的推荐准确度比基于词频-逆文档频率(TF-IDF)的推荐算法提高了28.9％,相对于传统LDA模型提高了43.8％.实验结果表明所提模型可有效应用于信息繁杂的不规则文本情感分析.%To solve the problem that traditional video emotional analysis methods can not work effectively and the results are not easy to explain,a video shot emotional analysis approach based on time-sync comments was proposed,as a basis for the recommendation of video shots.First,a formal description of video shots recommendation based on emotion analysis was studied.Then,after analyzing the classification of time sync comments based on Latent Dirichlet Allocation (LDA) topic model,the emotional vector of the words in time-sync comments were evaluated.Meanwhile,the emotion relationships among the video shots were analyzed for video shots recommendation.The recommendation precision of the proposed method was 28.9％ higher than that of the method based on Term Frequency-Inverse Document Frequency (TF-IDF),and 43.8％ higher than that of traditional LDA model.The experimental results show that the proposed model is effective in analyzing the complex emotion of different kinds of text information.