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This paper presents a new interactive opinion mining tool that helps users toclassify large sets of short texts originated from Web opinion polls, technicalforums or Twitter. From a manual multi-label pre-classification of a verylimited text subset, a learning algorithm predicts the labels of the remainingtexts of the corpus and the texts most likely associated to a selected label.Using a fast matrix factorization, the algorithm is able to handle largecorpora and is well-adapted to interactivity by integrating the correctionsproposed by the users on the fly. Experimental results on classical datasets ofvarious sizes and feedbacks of users from marketing services of thetelecommunication company Orange confirm the quality of the obtained results.

VIPE: A new interactive classification framework for large sets of short texts - application to opinion mining