VIPE: A new interactive classification framework for large sets of short texts - application to opinion mining
This provides a tool for users in fields like marketing to efficiently classify short texts from sources like Twitter or forums, though it appears incremental as it builds on existing matrix factorization methods.
The paper tackles the problem of classifying large sets of short texts for opinion mining by introducing VIPE, an interactive framework that uses a fast matrix factorization algorithm to predict labels from a small manual subset, with experimental results confirming its quality on various datasets and user feedback.
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.