@InProceedings{zhang-EtAl:2016:COLING4,
  author    = {Zhang, Dong  and  Li, Shoushan  and  Wang, Hongling  and  Zhou, Guodong},
  title     = {User Classification with Multiple Textual Perspectives},
  booktitle = {Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers},
  month     = {December},
  year      = {2016},
  address   = {Osaka, Japan},
  publisher = {The COLING 2016 Organizing Committee},
  pages     = {2112--2121},
  abstract  = {Textual information is of critical importance for automatic user classification
	in social media. However, most previous studies model textual features in a
	single perspective while the text in a user homepage typically possesses
	different styles of text, such as original message and comment from others. In
	this paper, we propose a novel approach, namely ensemble LSTM, to user
	classification by incorporating multiple textual perspectives. Specifically,
	our approach first learns a LSTM representation with a LSTM recurrent neural
	network and then presents a joint learning method to integrating all
	naturally-divided textual perspectives. Empirical studies on two basic user
	classification tasks, i.e., gender classification and age classification,
	demonstrate the effectiveness of the proposed approach to user classification
	with multiple textual perspectives.},
  url       = {http://aclweb.org/anthology/C16-1199}
}