Abstract
User profiling is an important research topic in social media analysis, which has great value in research and industries. Existing research on user profiling has mostly focused on manually handcrafted features for user attribute prediction. However, the research has partly overlooked the social relation of users. To address the problem, we propose a multi-granularity convolutional neural network model with feature fusion and refinement. Our model leverages the convolution mechanism to automatically extract user latent semantic features with respect to their attributes from social texts. We also combine different machine learning methods using the stacking mechanism for feature refinement. 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 based on the dataset from SMP CUP 2016 competition. The experimental results demonstrate that the proposed model is effective in automatic user attribute classification with a particular focus on fine-grained user information.
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Acknowledgements
This work is partially supported by a grant from the Foundation of State Key Laboratory of Cognitive Intelligence, iFLYTEK, P.R. China (COGOS-20190001, Intelligent Medical Question Answering based on User Profiling and Knowledge Graph), the Natural Science Foundation of China (No. 61632011, 61572102,61702080) and the Fundamental Research Funds for the Central Universities (No. DUT18ZD102), Postdoctoral Science Foundation of China (2018M641691), the Ministry of Education Humanities and Social Science Project (No. 19YJCZH199).
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Xu, B., Tadesse, M.M., Fei, P., Lin, H. (2019). Multi-granularity Convolutional Neural Network with Feature Fusion and Refinement for User Profiling. In: Zhang, Q., Liao, X., Ren, Z. (eds) Information Retrieval. CCIR 2019. Lecture Notes in Computer Science(), vol 11772. Springer, Cham. https://doi.org/10.1007/978-3-030-31624-2_13
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DOI: https://doi.org/10.1007/978-3-030-31624-2_13
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