Poetry can group: social relations among poets in Tang Dynasty based on Word2vec and social network

Yanbin Fang; Qinwei Li; Yuzhuo Liu; Jincheng Wei; Yiran Li

doi:10.1117/12.2620182

11 November 2021 Poetry can group: social relations among poets in Tang Dynasty based on Word2vec and social network

Yanbin Fang, Qinwei Li, Yuzhuo Liu, Jincheng Wei, Yiran Li

Proceedings Volume 12076, 2021 International Conference on Image, Video Processing, and Artificial Intelligence; 120760W (2021) https://doi.org/10.1117/12.2620182
Event: Fourth International Conference on Image, Video Processing, and Artificial Intelligence (IVPAI 2021), 2021, Shanghai, China

Abstract

The study sought to dig deeply into the social relations of poets in Tang Dynasty through Tang poetry. Based on Word2vec, we trained the corpus of Tang poetry and generated the word vector, thus WMD was used to calculate the poetic similarity to reflect the poetic style; we use Gephi to construct the poets’ social relation network and use network analysis algorithms, including Community detection, Network shortcuts, etc to quantify the integral and partial attributes of the networks and to analyze the characteristics of "poetic social" and how the poet's own attributes affect the poet social on the macroscopic and microcosmic levels. Furthermore, poetic similarity were used to analyze the evolution characteristics of poetry styles under the influence of "poetic social". The study shows that there are some phenomena such as small groups and long tail effect in poets' social interaction; the social closeness of poets is related to the attributes of poets to varying extent; the social relations and poetic styles influence each other in development.

Citation Download Citation

Yanbin Fang, Qinwei Li, Yuzhuo Liu, Jincheng Wei, and Yiran Li "Poetry can group: social relations among poets in Tang Dynasty based on Word2vec and social network", Proc. SPIE 12076, 2021 International Conference on Image, Video Processing, and Artificial Intelligence, 120760W (11 November 2021); https://doi.org/10.1117/12.2620182

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KEYWORDS

Social networks

Lithium

Data processing

Data modeling

Network security

Statistical analysis

Visualization

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