Research on emotional space for movie and TV drama videos

作     者：Li Yujie Zhang Jingjing Jiang Wei Wang Chunxiao Li Yujie;Zhang Jingjing;Jiang Wei;Wang Chunxiao

作者机构：Key Laboratory of Acoustic Visual Technology and Intelligent Control SystemCommunication University of ChinaBeijing 100024China Beijing Key Laboratory of Modern Entertainment TechnologyCommunication University of ChinaBeijing 100024China School of Information and Communication EngineeringCommunication University of ChinaBeijing 100024China School of Computer and Cyber SciencesCommunication University of ChinaBeijing 100024China 

出 版 物：《The Journal of China Universities of Posts and Telecommunications》 (中国邮电高校学报（英文版）)

年 卷 期：2022年第29卷第5期

页      面：73-82页

核心收录：

学科分类：13[艺术学] 1303[艺术学-戏剧与影视学] 08[工学] 081203[工学-计算机应用技术] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：supported by the Key Laboratory of the Ministry of Culture and Tourism (WLBSYS2005) the Fundamental Research Funds for the Central Universities (CUC19ZD005)。 

主　　题：emotional space movie and TV drama videos subjective evaluation words semantic analysis fusion space 

摘      要：Emotional space refers to a multi-dimensional emotional model that describes a group of subjective feelings or emotions. Since the existing discrete emotional space is mainly aimed at human’s primary emotions, it cannot describe the complex emotions evoked when watching movies. In order to solve this problem, an emotional fusion space for videos was constructed by selecting movies and TV dramas with rich emotional semantics as the research objects. Firstly, emotional words based on movie and TV drama videos are acquired and analyzed by using subjective evaluation and semantic analysis methods. Then, the emotional word vectors obtained from the above analysis are fused, reduced dimension by t-distributed stochastic neighbor embedding(t-SNE) algorithm, and clustered by bisecting K-means clustering algorithm to get a discrete emotional space for movie and TV drama videos. This emotional fusion space can obtain different categories by changing the value of the emotion classification number without re-labeling and calculation.