Modeling Online Collective Emotions Through Knowledge Transfer
Saike He1; Xiaolong Zheng1; Daniel Zeng1,2
2017-08-18
会议名称IEEE Intelligence and Security Informatics 2017 Conference (ISI 2017)
会议录名称Intelligence and Security Informatics (ISI), 2017 IEEE International Conference on
会议日期22-24 July 2017
会议地点Beijing, China
摘要Online emotion diffusion is a compound process that involves interactions with multiple modalities. For instance, different behaviors influence the velocity and scale of emotion diffusion in online communities. Depicting and predicting massive online emotions helps to guide the trend of emotion evolution, thus avoiding unprecedented damages in crises. However, most existing work tries to depict and predict online emotions based on models not considering related modalities. There still lacks an efficient modeling framework that promotes performance by leveraging multi-modality knowledge, and quantifies the interactions among different modalities. In this paper, we elaborate a computational model to jointly depict online emotions and behaviors. By introducing a common structure, we can quantify how user emotions interact with the corresponding behaviors. To scale up to large dataset, we propose a hierarchical optimization algorithm to accelerate the convergence of the model. Evaluation on Sina Weibo dataset suggests that prediction error rate is lowered by 69 percent with the proposed model. In addition, the proposed model helps to explain how user emotions influence consequent behaviors in extreme situations.
关键词Online Emotions Knowledge Transfer Social Crises
DOI10.1109/ISI.2017.8004909
引用统计
文献类型会议论文
条目标识符http://ir.ia.ac.cn/handle/173211/15356
专题复杂系统管理与控制国家重点实验室_互联网大数据与信息安全
通讯作者Xiaolong Zheng
作者单位1.The State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
2.University of Chinese Academy of Sciences, Beijing, China
推荐引用方式
GB/T 7714
Saike He,Xiaolong Zheng,Daniel Zeng. Modeling Online Collective Emotions Through Knowledge Transfer[C],2017.
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