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

[1] WEN K M,XU S,LI R X,et al.Survey of Microblog and Chinese Microblog Information Processing[J].Journal of Chinese Information Processing,2012,6(6):28-36.(in Chinese) 文坤梅,徐帅,李瑞轩,等.微博及中文微博信息处理研究综述[J].中文信息学报,2012,6(6):28-36.
[2] ZHANG J F,XIA Y Q,YAO J M.A Review towards Microtext Processing[J].Journal of Chinese Information Processing,2012,6(4):21-27.(in Chinese) 张剑锋,夏云庆,姚建民.微博文本处理研究综述[J].中文信息学报,2012,6(4):21-27.
[3] WANG J J,LI S S,HUANG L.User Gender Classification in Chinese Microblog[J].Journal of Chinese Information Processing,2014,8(6):150-155.(in Chinese) 王晶晶,李寿山,黄磊.中文微博用户性别分类方法研究[J].中文信息学报,2014,8(6):150-155.
[4] DICKINSON M B,HU W.Gender Prediction on Twitter Using Stream Algorithms with N-Gram Character Features[J].Proceedings of International Journal of Intelligences Science,2012,2(4):143-148.
[5] MORGAN M S,DEREK R.Gender Inference of Twitter Users in Non-English Contexts[C]∥Proceedings of EMNLP.2013:1136-1145.
[6] GONCALVES C B,RATIKIEWICZ J,FLAMMINI A,et al.Predicting the political alignment of Twitter user[C]∥Procee-dings of the International Conference on Social Computing.2011.
[7] LIU,RUTHS D.What’s in a name? Using first names as features for gender inference in Twitter[C]∥Analyzing Microtext:2013 AAAI Spring Symposium.2013.
[8] EICHSTAEDT M C,KERN L,et al.Developing Age and Gender Predictive Lexica over Social Media[C]∥Proceedings of EMNLP.2014:1146-1151.
[9] FARNADI M G,VASUDEVAN G,DAVALOS S,et al.Ageand gender identification in social media[C]∥Proceedings of CLEF 2014 Evaluation Labs pages.2014:1129-1136.
[10] HOCHREITER,JURGEN S.Long Short-Term Memory[J].Neural Computation,1997,9(8):1735-1780.
[11] GRAVES A.Generating Sequences With Recurrent Neural Networks[J].arXiv preprint arXiv:1308.0850,2013.
[12] ANTOINE X B,YOSHUA B.Deep Sparse Rectifier NeuralNetworks[C]∥Proceedings of AISTATS.2011:315-323.
[13] HINTON G E,SRIVASTAVA N,KRIZHEVSKY A,et al.Improving Neural Networks by Preventing Co-adaptation of Feature Detectors[J].Computer Science,2012,3(4):212-223.

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