Multi-dimensional user appeal topic identification method based on semantic analysis and PageRank algorithm

Xiaoyi Huang; Feng Zhao; Chenqi Hu

doi:10.1117/12.3035411

19 July 2024 Multi-dimensional user appeal topic identification method based on semantic analysis and PageRank algorithm

Xiaoyi Huang, Feng Zhao, Chenqi Hu

Author Affiliations +

Proceedings Volume 13213, International Conference on Image Processing and Artificial Intelligence (ICIPAl 2024); 132131B (2024) https://doi.org/10.1117/12.3035411
Event: International Conference on Image Processing and Artificial Intelligence (ICIPAl2024), 2024, Suzhou, China

Abstract

Facing the problem that the potential complaint risks existing in a large number of service work orders have not been fully explored, this paper puts forward an intelligent analysis method of power supply service demands based on big data. According to the support of the data set, the semantic analysis and PageRank algorithm mining protocol are followed to preprocess the theme information of multi-dimensional user demands; According to the pre-processing results, the step flow of multi-dimensional user appeal theme identification is designed to realize the research on multi-dimensional user appeal theme identification method. The experimental results show that the recognition rate and recall rate of the proposed method are high, which can ensure good and stable recognition results and ensure recognition accuracy and efficiency. Therefore, the multi-dimensional user appeal theme identification method is practical. So PageRank can be applied in multi-dimensional user attraction theme recognition.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Xiaoyi Huang, Feng Zhao, and Chenqi Hu "Multi-dimensional user appeal topic identification method based on semantic analysis and PageRank algorithm", Proc. SPIE 13213, International Conference on Image Processing and Artificial Intelligence (ICIPAl 2024), 132131B (19 July 2024); https://doi.org/10.1117/12.3035411

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