AbstractIn 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.