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 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 multilabel classification, (ii) MNB with one-vsrest 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 3
rd and 5 the position for Japanese and
French, respectively, among all submitted systems
