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SCIENTIA SINICA Informationis, Volume 47 , Issue 8 : 1095-1108(2017) https://doi.org/10.1360/N112016-00278

User age prediction by combining classification and regression}{User age prediction by combining classification and regression

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  • ReceivedMar 30, 2017
  • AcceptedJun 8, 2017
  • PublishedAug 16, 2017

Abstract


Funded by

国家自然科学基金(61331011)

国家自然科学基金(61375073)

国家自然科学基金(61672366)


References

[1] Preotiuc-Pietro D, Lampos V, Aletras N. An analysis of the user occupational class through twitter content. In: Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics. Pennsylvania: Association for Computational Linguistics, 2015. 1754-1764. Google Scholar

[2] Volkova S, Wilson T, Yarowsky D. Exploring demographic language variations to improve multilingual sentiment analysis in social media. In: Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing. Pennsylvania: Association for Computational Linguistics, 2013. 1815-1827. Google Scholar

[3] O'Connor B, Balasubramanyan R, Routledge B R, et al. From tweets to polls: linking text sentiment to public opinion time series. In: Proceedings of the 4th International Conference on Weblogs and Social Media. California: AAAI Press, 2010. 1842-1850. Google Scholar

[4] Schler J, Koppel M, Argamon S, et al. Effects of age and gender on blogging. Front Inform Tech Electron Eng, 2006, 274: 199-205. Google Scholar

[5] Burger J D, Henderson J C. An exploration of observable features related to blogger age. In: Proceedings of the 2006 AAAI Spring Symposium on Computational Approaches to Analyzing Weblogs. California: AAAI Press, 2006. 15-20. Google Scholar

[6] Nguyen D, Smith N A, Rose C. Author age prediction from text using liner regression. In: Proceedings of the 5th ACL-HLT Workshop on Language Technology for Cultural Heritage, Social Sciences, and Humanities. Pennsylvania: Association for Computational Linguistics, 2011. 115-123. Google Scholar

[7] Nguyen D, Gravel R, Trieschnigg D, et al. ``How old do you think I am?": a study of language and age in twitter. In: Proceedings of the 7th International Conference on Weblogs and Social Media. California: AAAI Press, 2013. 439-448. Google Scholar

[8] Tang D, Qin B, Liu T. Aspect level sentiment classification with deep memory network. In: Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing. Pennsylvania: Association for Computational Linguistics, 2016. 214-224. Google Scholar

[9] Barone A V M, Attardi G. Non-projective dependency-based pre-reordering with recurrent neural network for machine translation. In: Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics. Pennsylvania: Association for Computational Linguistics, 2015. 846-856. Google Scholar

[10] Ikeda D, Takamura H, Okumura M. Semi-supervised learning for blog classification. In: Proceedings of the 23rd AAAI Conference on Artificial intelligence. California: AAAI Press, 2008. 1156-1164. Google Scholar

[11] Rosenthal S, McKeown K. Age prediction in blogs: a study of style, content, and online behavior in pre- and post-social media generations. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics, Portland, 2011. 763-772. Google Scholar

[12] Mackinnon I, Warren R H. Statistical Network Analysis: Models, Issues, and New Directions. Berlin: Springer, 2006. Google Scholar

[13] Peersman C, Daelemans W, Vaerenbergh L V. Predicting age and gender in online social networks. In: Proceedings of the 3rd International Workshop on Search and Mining User-generated Contents. New York: ACM, 2011. 37-44. Google Scholar

[14] Marquardt J, Farnadi G, Vasudevan G, et al. Age and gender identification in social media. In: Proceedings of the 5th Conference and Labs of the Evaluation Forum (CLEF 2014), Sheffield, 2014. 1129-1136. Google Scholar

[15] Chen J, Li S S, Dai B, et al. Active learning for age regression in social media. In: Proceedings of China National Conference on Chinese Computational Linguistics. Berlin: Springer, 2016. 351-362. Google Scholar

[16] Hochreiter S, Schmidhuber J. Long Short-Term Memory. \href{https://doi.org/10.1162/neco.1997.9.8.1735}{Neural Computation}, 1997, 9: 1735-1780. Google Scholar

[17] Graves A. Generating sequences with recurrent neural networks, arXiv: \href{https://arxiv.org/abs/1308.0850, 2013}{1308.0850, 2013}. Google Scholar

[18] Hinton G E, Srivastava N, Krizhevsky A, et al. Improving neural networks by preventing co-adaptation of feature detectors. Comput Sci, 2012, 3: 212-223. Google Scholar

[19] LeCun Y A, Bottou L, Orr G B, et al. Efficient backprop. Neur Net Tricks Trade, 2012, 1524: 9-50. Google Scholar

[20] Cameron A C, Windmeijer F A G. R-squared measures for count data regression models with applications to health-care utilization. J Bus Econ Stat, 1996, 14: 209-220. Google Scholar

[21] Johnson R, Zhang T. Effective use of word order for text categorization with convolutional neural networks. In: Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics. Pennsylvania: Association for Computational Linguistics, 2015. 103-112. Google Scholar

[22] Agarwal B, Sharma V K, Mittal N. Sentiment classification of review documents using phrase patterns. In: Proceedings of International Conference on Advances in Computing, Communications and Informatics. New York: IEEE, 2013. 1577-1580. Google Scholar

[23] Elkouri A. Predicting the sentiment polarity and rating of yelp reviews, arXiv: \href{https://arxiv.org/abs/1512.06303, 2015}{1512.06303, 2015}. Google Scholar