Prédiction de modèles structurés d'opinion : aspects théoriques et méthodologiques. (Prediction of structured opinion outputs : theoretical and methodological aspects)

Opinion mining has emerged as a hot topic in the machine learning community due to the recent availability of large amounts of opinionated data expressing customer's attitude towards merchandisable goods. Yet, predicting opinions is not easy due to the lack of computational models able to capture the complexity of the underlying objects at hand. Current approaches consist in predicting simple representations of the affective expressions, for example by restricting themselves to the valence attribute. This thesis focuses on the question of building structured output models able to jointly predict the different components of opinions in order to take advantage of the dependency between their parts. In this context, the choice of an opinion model has some consequences on the complexity of the learning problem and the statistical properties of the resulting predictors. We study 2 classical problems of opinion mining in which we instantiate squared surrogate based structured output learning techniques to illustrate the accuracy-complexity tradeoff arising when building opinion predictors. A second aspect of this thesis is to handle a newly released multimodal dataset containing entity and valence annotations at different granularity levels providing a complex representation of the underlying expressed opinions. We propose a deep learning based approach able to take advantage of the different labeled parts of the output objects by learning to jointly predict them. We propose a novel hierarchical architecture composed of different state-of-the-art multimodal neural layers and study the effect of different learning strategies in this joint prediction context. The resulting model is shown to improve over the performance of separate opinion component predictors and raises new questions concerning the optimal treatment of hierarchical labels in a structured prediction context. This thesis focuses on the question of building structured output models able to jointly predict the different components of opinions in order to take advantage of the dependency between their parts. In this context, the choice of an opinion model has some consequences on the complexity of the learning problem and the statistical properties of the resulting predictors. We specifically analyzed the case of preference based learning and joint entity and valence detection under a 2 layer binary tree representation in order to derive excess risk bounds and an analysis of the learning procedure algorithmic complexity. In these two settings, the output objects can be decomposed over a set of interacting parts with radical differences. However, we treat both problems under the same angle of squared surrogate based structured output learning and discuss the specificities of the two problem specifications. A second aspect of this thesis is to handle a newly released multimodal dataset containing entity and valence annotations at different granularity levels providing a complex representation of the underlying expressed opinions. In this context of large scale multimodal data with multiple granularity annotations, designing a dedicated model is quite challenging. Hence, we propose a deep learning based approach able to take advantage of the different labeled parts of the output objects by learning to jointly predict them. We propose a novel hierarchical architecture composed of different state-of-the-art multimodal neural layers and study the effect of different learning strategies in this joint prediction context. The resulting model is shown to improve over the performance of separate opinion component predictors and raises new questions concerning the optimal treatment of hierarchical labels in a structured prediction context.