Abstract
Emotion is a status that combines people’s feelings, thoughts, and behaviors and plays a crucial role in the communication between people. Considerable study suggests that human emotions can also be conveyed through online interactions. For a systematical literature review, we find that few studies focus on the influence of some special users on the process of emotional transmission in online social networks. To fill this gap, we first introduce the definition of emotion role, they are special users who play an important role in the process of emotion contagion of online social networks. We then propose an Emotion Role Mining approach (ERM) to detect users’ emotion roles in social networks. A set of features and measures is proposed and calculated to identify and represent these users based on the analysis of their emotion influence and long-term emotional preferences. Experiments and evaluations are conducted to demonstrate the practicability and usefulness of the proposed approach using Micro-blog data. Comparison experiments indicate that the proposed approach outperforms several baseline methods.
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Acknowledgement
This work was supported by the National Natural Science Foundation (Grant Nos. 61872298, 61532009, 61802316, and 61902324), the Innovation Fund Of Postgraduate, Xihua University (Grant Nos. ycjj2019023).
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Wang, Y., Du, Y., Xie, C. (2021). Emotion Role Identification in Social Network. In: Chen, H., Liu, K., Sun, Y., Wang, S., Hou, L. (eds) Knowledge Graph and Semantic Computing: Knowledge Graph and Cognitive Intelligence. CCKS 2020. Communications in Computer and Information Science, vol 1356. Springer, Singapore. https://doi.org/10.1007/978-981-16-1964-9_23
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DOI: https://doi.org/10.1007/978-981-16-1964-9_23
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