@InProceedings{lohar-EtAl:2017:I17-4,
  author    = {Lohar, Pintu  and  Dutta Chowdhury, Koel  and  Afli, Haithem  and  Hasanuzzaman, Mohammed  and  Way, Andy},
  title     = {ADAPT at IJCNLP-2017 Task 4: A Multinomial Naive Bayes Classification Approach for Customer Feedback Analysis task},
  booktitle = {Proceedings of the IJCNLP 2017, Shared Tasks},
  month     = {December},
  year      = {2017},
  address   = {Taipei, Taiwan},
  publisher = {Asian Federation of Natural Language Processing},
  pages     = {161--169},
  abstract  = {In this age of the digital economy, promoting organisations attempt their best
	to engage the customers in the feedback provisioning process. With the
	assistance of customer insights, an organisation can develop a better product
	and provide a better service to its customer. In this paper, we analyse the
	real world samples of customer feedback from Microsoft Office customers in four
	languages, i.e., English, French, Spanish and Japanese and conclude a
	five-plus-one-classes categorisation (comment, request, bug, complaint,
	meaningless and undetermined) for meaning classification. The task is to
	%access multilingual corpora annotated by the proposed meaning categorization
	scheme and develop a system to
	determine what class(es) the customer feedback sentences should be annotated as
	in four languages. We propose following approaches to accomplish this task: 
	 (i) a multinomial naive bayes (MNB) approach for multi-label classification, 
	 (ii) MNB with one-vs-rest classifier approach, and 
	 (iii) the combination of the multilabel classification-based and the sentiment
	classification-based approach. 
	Our best system produces F-scores of 0.67, 0.83, 0.72 and 0.7 for English,
	Spanish, French and Japanese, respectively. The results are competitive to the
	best ones for all languages and secure 3rd and 5th position for Japanese and
	French, respectively, among all submitted systems.},
  url       = {http://www.aclweb.org/anthology/I17-4027}
}