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Improving User Attribute Classification with Text and Social Network Attention

  • Yumeng Li
  • Liang Yang
  • Bo Xu
  • Jian Wang
  • Hongfei LinEmail author
Article
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Abstract

User attribute classification is an important research topic in social media user profiling, which has great commercial value in modern advertisement systems. Existing research on user profiling has mostly focused on manually handcrafted features for different attribute classification tasks. However, these research has partly overlooked the social relation of users. We propose an end-to-end neural network model called the social convolution attention neural network. Our model leverages the convolution attention mechanism to automatically extract user features with respect to different attributes from social texts. The proposed model can capture the social relation of users by combining semantic context and social network information, and improve the performance of attribute classification. We evaluate our model in the gender, age, and geography classification tasks based on the dataset from SMP CUP 2016 competition, respectively. The experimental results demonstrate that the proposed model is effective in automatic user attribute classification with a particular focus on fine-grained user information. We propose an effective model based on the convolution attention mechanism and social relation information for user attribute classification. The model can significantly improve the accuracy in various user attribute classification tasks.

Keywords

User attribute classification Social media Convolution neural network Attention mechanism 
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Notes

Acknowledgments

This work is partially supported by grant from the Natural Science Foundation of China (Nos. 61632011, 61572102, 61772103, 61702080, 61602078), the Ministry of Education Humanities and Social Science Project (No. 16YJCZH12), the Fundamental Research Funds for the Central Universities (DUT18ZD102), and the National Key Research Development Program of China (No. 2016YFB1001103). China Postdoctoral Science Foundation (No. 2018M641691).

Compliance with Ethical Standards

Conflict of interests

The authors declare that they have no conflicts of interest.

Informed Consent

All procedures followed were in accordance with the ethical standards of the responsible committee on human experimentation (institutional and national) and with the Helsinki declaration of 1975, as revised in 2008(5). Additional informed consent was obtained from all patients for which identifying information is included in this article.

Human and Animal Rights

This article does not contain any studies with human participants or animals performed by any of the authors.

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  • Yumeng Li
    • 1
  • Liang Yang
    • 1
  • Bo Xu
    • 1
  • Jian Wang
    • 1
  • Hongfei Lin
    • 1
    Email author
  1. 1.Dalian University of TechnologyDalianChina

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