@InProceedings{gupta-EtAl:2017:I17-4,
  author    = {Gupta, Deepak  and  Lenka, Pabitra  and  Bedi, Harsimran  and  Ekbal, Asif  and  Bhattacharyya, Pushpak},
  title     = {IITP at IJCNLP-2017 Task 4: Auto Analysis of Customer Feedback using CNN and GRU Network},
  booktitle = {Proceedings of the IJCNLP 2017, Shared Tasks},
  month     = {December},
  year      = {2017},
  address   = {Taipei, Taiwan},
  publisher = {Asian Federation of Natural Language Processing},
  pages     = {184--193},
  abstract  = {Analyzing customer feedback is the best
	way to channelize the data into new marketing
	strategies that benefit entrepreneurs
	as well as customers. Therefore an automated
	system which can analyze the
	customer behavior is in great demand.
	Users may write feedbacks in any language,
	and hence mining appropriate information
	often becomes intractable. Especially
	in a traditional feature-based supervised
	model, it is difficult to build
	a generic system as one has to understand
	the concerned language for finding
	the relevant features. In order to
	overcome this, we propose deep Convolutional
	Neural Network (CNN) and Recurrent
	Neural Network (RNN) based approaches
	that do not require handcrafting
	of features. We evaluate these techniques
	for analyzing customer feedback
	sentences on four languages, namely English,
	French, Japanese and Spanish. Our
	empirical analysis shows that our models
	perform well in all the four languages
	on the setups of IJCNLP Shared Task on
	Customer Feedback Analysis. Our model
	achieved the second rank in French, with
	an accuracy of 71.75% and third ranks for
	all the other languages.},
  url       = {http://www.aclweb.org/anthology/I17-4031}
}