JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE) ›› 2017, Vol. 52 ›› Issue (7): 91-96.doi: 10.6040/j.issn.1671-9352.1.2016.019

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User age regression with dual-channel LSTM

CHEN Jing, LI Shou-shan*, ZHOU Guo-dong   

  1. Natural Language Processing Lab, Soochow University, Suzhou 215006, Jiangsu, China
  • Received:2016-12-09 Online:2017-07-20 Published:2017-07-07

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Abstract: Traditional age regression approach cant learn context relation, so we utilize deep learning approach which could make full use of context relation to predict users age. Specific implementation is that we propose a age regression approach based on LSTM. LSTM can learn long short-term memory, namely building long relevant connection between input values. We utilize two different features, namely textual and social features. In order to distinguish the two features and make full use of them, we propose a new age regression approach based on dual-channel LSTM. Specific implementation is to add a Merge layer into LSTM, combing text features representation and social features representation generated by LSTM, to fully learn knowledge between textual and social features. Experimental results show that our method can effectively distinguish textual and social features and improve the performance of age regression.

Key words: LSTM, textual features, age regression, social features

CLC Number: 

  • TP391
[1] SCHLER J, KOPPEL M, ARGAMON S, et al. Effects of age and gender on blogging[J]. Frontiers of Information Technology & Electronic Engineering, 2006, 274(s 1/2):199-205.
[2] BURGER J D, HENDERSON J C. An exploration of observable features related to blogger age[C] //Proceedings of AAAI Spring Symposium on Computational Approaches to Analyzing Weblogs. California: AAAI Press, 2006: 15-20.
[3] NGUYEN D, SMITH N A, ROSE C P. Author age prediction from text using liner regression[J]. Acl-hlt Workshop on Language Technology for Cultural Heritage, 2011, 39(4): 115-123.
[4] NGUYEN D P, GRAVEL R, TRIESCHNIGG R B, et al. “How old do you think i am?”: a study of language and age in twitter[C] //Proceedings of AAAI Conference on Weblogs and Social Media. California: AAAI Press, 2013: 439-448.
[5] IKEDA D, TAKAMURA H, OKUMURA M. Semi-supervised learning for blog classification[C] //Proceedings of the 23rd AAAI Conference on Artificial Intelligence. California: AAAI Press, 2008, 89: 1156-1161.
[6] ROSENTHAL S, MCKEOWN K. Age prediction in blogs: a study of style, content, and online behavior in pre-and post-social media generations[C] //Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics. Stroudsburg: Association for Computational Linguistics, 2011: 763-772.
[7] MACKINNON I, WARREN R H. Age and geographic inferences of the live journal social network[J]. Springer Berlin Heidelberg, 2007, 4503: 176-178.
[8] PEERSMAN C, DAELEMANS W, VAERENBERGH L V. Predicting age and gender in online social networks[C] //Proceedings of the 3rd International Workshop on Search and Mining User-generated Contents SMUC-11. New York: ACM, 2011: 37-44.
[9] Marquardt J, Farnadi G, Vasudevan G, et al. Age and gender identification in social media[C] //Proceedings of CLEF 2014 Evaluation Labs Pages. Sheffield: CEUR Workshop Proceedings, 2014: 1129-1136.
[10] 薛云霞. 微博用户属性识别方法研究[D]. 苏州: 苏州大学研究生院, 2015. XUE Yunxia. Research on microblog user atributes recognition[D]. Suzhou: Graduate School of Soochow University, 2015.
[11] CHEN Jing, LI Shoushan, DAI Bin, et al. Active learning for age regression in social media[C] //Chinese Computational Linguistics and Natural Language Processing Based on Naturally Annotated Big Data. Berlin: Springer, 2016: 351-362.
[12] HOCHREITER S, SCHMIDHUBER J. Long short-term memory[J]. Neural Computation, 1997, 9(8): 1735-1780.
[13] GRAVES A. Generating sequences with recurrent neural networks[J]. Computer Science, 2013(1308.0850): 1-43.
[14] HINTON G E, SRIVASTAVA N, KRIZHEVSKY A, et al. Improving neural networks by preventing co-adaptation of feature detectors[J]. ResearchGate, 2012, 3(4): 212-223.
[15] LECUN Y A, BOTTOU L, ORR G B, et al. Efficient backprop[M]. Berlin: Springer, 2012: 9-48.
[16] CAMERON A C, WINDMEIJER F A G. R-squared measures for count data regression models with applications to health-care utilization[J]. Journal of Business and Economic Statistics, 1996, 14(2): 209-220.
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