VIPE: A NEW INTERACTIVE CLASSIFICATION FRAMEWORK FOR LARGE SETS OF SHORT TEXTS - APPLICATION TO OPINION MINING

Wissam Siblini (1, 2) , Frank Meyer (3) , Pascale Kuntz (1, 2)

1 LS2N - Laboratoire des Sciences du Numérique de Nantes
2 LS2N - équipe DUKe - Data User Knowledge
3 Orange Labs [Lannion]

This paper presents a new interactive opinion mining tool that helps users to classify large sets of short texts originated from Web opinion polls, technical forums or Twitter. From a manual multi-label pre-classification of a very limited text subset, a learning algorithm predicts the labels of the remaining texts of the corpus and the texts most likely associated to a selected label. Using a fast matrix factorization, the algorithm is able to handle large corpora and is well-adapted to interactivity by integrating the corrections proposed by the users on the fly. Experimental results on classical datasets of various sizes and feedbacks of users from marketing services of the telecommunication company Orange confirm the quality of the obtained results.

Mots clés: opinion mining; interactive learning; multi-label classification

Domaines: Informatique [cs]

Wissam Siblini, Frank Meyer, Pascale Kuntz. VIPE: A NEW INTERACTIVE CLASSIFICATION FRAMEWORK FOR LARGE SETS OF SHORT TEXTS - APPLICATION TO OPINION MINING. 47th International Conference on Computers & Industrial Engineering (CIE47), Oct 2017, Lisbon, Portugal. ⟨hal-01624602⟩