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The task of predicting fine grained user opinion based on spontaneous spokenlanguage is a key problem arising in the development of Computational Agents aswell as in the development of social network based opinion miners.Unfortunately, gathering reliable data on which a model can be trained isnotoriously difficult and existing works rely only on coarsely labeledopinions. In this work we aim at bridging the gap separating fine grainedopinion models already developed for written language and coarse grained modelsdeveloped for spontaneous multimodal opinion mining. We take advantage of theimplicit hierarchical structure of opinions to build a joint fine and coarsegrained opinion model that exploits different views of the opinion expression.The resulting model shares some properties with attention-based models and isshown to provide competitive results on a recently released multimodal finegrained annotated corpus.

From the Token to the Review: A Hierarchical Multimodal approach to Opinion Mining