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 classificationbased
and the sentiment classificationbased
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
th position for Japanese and
French, respectively, among all submitted
systems.