  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 in 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.
