@InProceedings{assawinjaipetch-EtAl:2016:WLSI-OIAF4HLT2016,
  author    = {assawinjaipetch, panuwat  and  Shirai, Kiyoaki  and  Sornlertlamvanich, Virach  and  Marukata, Sanparith},
  title     = {Recurrent Neural Network with Word Embedding for Complaint Classification},
  booktitle = {Proceedings of the Third International Workshop on Worldwide Language Service Infrastructure and Second Workshop on Open Infrastructures and Analysis Frameworks for Human Language Technologies (WLSI/OIAF4HLT2016)},
  month     = {December},
  year      = {2016},
  address   = {Osaka, Japan},
  publisher = {The COLING 2016 Organizing Committee},
  pages     = {36--43},
  abstract  = {Complaint classification aims at using information to deliver greater insights
	to enhance user experience after purchasing the products or services.
	Categorized information can help us quickly collect emerging problems in order
	to provide a support needed. Indeed, the response to the complaint without the
	delay will grant users highest satisfaction. In this paper, we aim to deliver a
	novel approach which can clarify the complaints precisely with the aim to
	classify each complaint into nine predefined classes i.e. acces-sibility,
	company brand, competitors, facilities, process, product feature, staff
	quality, timing respec-tively and others. Given the idea that one word usually
	conveys ambiguity and it has to be interpreted by its context, the word
	embedding technique is used to provide word features while applying deep
	learning techniques for classifying a type of complaints. The dataset we use
	contains 8,439 complaints of one company.},
  url       = {http://aclweb.org/anthology/W16-5205}
}