Modeling Online Collective Emotions Through Knowledge Transfer
文献类型：会议论文
| 作者 | Saike He1 ; Xiaolong Zheng1; Daniel Zeng1,2
|
| 出版日期 | 2017-08-18 |
| 会议日期 | 22-24 July 2017 |
| 会议地点 | Beijing, China |
| 关键词 | Online Emotions Knowledge Transfer Social Crises |
| DOI | 10.1109/ISI.2017.8004909 |
| 英文摘要 | 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. |
| 会议录 | Intelligence and Security Informatics (ISI), 2017 IEEE International Conference on
 |
| 源URL | [http://ir.ia.ac.cn/handle/173211/15356]  |
| 专题 | 自动化研究所_复杂系统管理与控制国家重点实验室_互联网大数据与安全信息学研究中心 |
| 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]. 见:. Beijing, China. 22-24 July 2017. |
入库方式： OAI收割
来源：自动化研究所
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