@InProceedings{lin-EtAl:2017:I17-42,
  author    = {Lin, Shuying  and  Xie, Huosheng  and  Yu, Liang-Chih  and  Lai, K. Robert},
  title     = {SentiNLP at IJCNLP-2017 Task 4: Customer Feedback Analysis Using a Bi-LSTM-CNN Model},
  booktitle = {Proceedings of the IJCNLP 2017, Shared Tasks},
  month     = {December},
  year      = {2017},
  address   = {Taipei, Taiwan},
  publisher = {Asian Federation of Natural Language Processing},
  pages     = {149--154},
  abstract  = {The analysis of customer feedback is useful to provide good customer service.
	There are a lot of online customer feedback are produced. Manual classification
	is impractical because the high volume of data. Therefore, the automatic
	classification of the customer feedback is of importance for the analysis
	system to identify meanings or intentions that the customer express. The aim of
	shared Task 4 of IJCNLP 2017 is to classify the customer feedback into six tags
	categorization. In this paper, we present a system that uses word embeddings to
	express the feature of the sentence in the corpus and the neural network as the
	classifier to complete the shared task. And then the ensemble method is used to
	get final predictive result. The proposed method get ranked first among twelve
	teams in terms of micro-averaged F1 and second for accura-cy metric.},
  url       = {http://www.aclweb.org/anthology/I17-4025}
}