Abstract
The last decades have witnessed the booming of social networks where people interact with each other and generate an unprecedented amount of content. With the online information diffusion, users publish microblog which can affect the sentiments changes of others. The aim of this paper is to analyze the sentimental influence on Sina Microblog users. In this paper, the CNN+ELM model is first proposed, in which the convolution neural network (CNN) and the extreme learning machine (ELM) are combined to identify the sentiment trend of the content. And then, the local sentiment influence rank (LSenInfRank) algorithm and the global sentiment influence rank (GSenInfRank) algorithm based on the sentiment polarity of the content, the user interactive information and the network topology, are proposed. The experimental results on the real Sina Microblog dataset show that the proposed algorithms can effectively find out the set of users with large sentimental influence and have good stability.
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Acknowledgement
This work was supported by the National key R&D Program of China (2016YFC1401900); National Natural Science Foundation of China (61173029, 61672144).
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Han, D., Wei, F., Bai, L., Tang, X., Zhu, T., Wang, G. (2020). An Algorithm of Sina Microblog User’s Sentimental Influence Analysis Based on CNN+ELM Model. In: Cao, J., Vong, C., Miche, Y., Lendasse, A. (eds) Proceedings of ELM 2018. ELM 2018. Proceedings in Adaptation, Learning and Optimization, vol 11. Springer, Cham. https://doi.org/10.1007/978-3-030-23307-5_10
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DOI: https://doi.org/10.1007/978-3-030-23307-5_10
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