Computer Science ›› 2018, Vol. 45 ›› Issue (2): 121-124.doi: 10.11896/j.issn.1002-137X.2018.02.021

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User Gender Classification with Dual-channel LSTM

WANG Li-min, YAN Qian, LI Shou-shan and ZHOU Guo-dong   

  • Online:2018-02-15 Published:2018-11-13

Abstract: User gender classification aims at classifying the users into male and female with the provided information.Previous studies on gender classification mainly focus on a single type of features (i.e.,textual features or social features).Different from previous research,this paper proposed a new approach named dual-channel LSTM by making full use of the relationship between textual features (the text which user publishes) and social features (the followers which user concerns).Specifically,this paper first got two kinds of features using single-channel LSTM respectively.Then,it proposed a joint learning method to integrate the features.Lastly,it got the final classification results by the dual-channel LSTM.Empirical studies show that the dual-channel LSTM model achieves effective results for gender classification compared with traditional classification algorithms.

Key words: Gender classification,Sina weibo,Dual-channel LSTM

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