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

Abstract : The task of predicting fine grained user opinion based on spontaneous spoken language is a key problem arising in the development of Computational Agents as well as in the development of social network based opinion miners. Unfortunately, gathering reliable data on which a model can be trained is notoriously difficult and existing works rely only on coarsely labeled opinions. In this work we aim at bridging the gap separating fine grained opinion models already developed for written language and coarse grained models developed for spontaneous multimodal opinion mining. We take advantage of the implicit hierarchical structure of opinions to build a joint fine and coarse grained opinion model that exploits different views of the opinion expression. The resulting model shares some properties with attention-based models and is shown to provide competitive results on a recently released multimodal fine grained annotated corpus.
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Contributor : Alexandre Garcia <>
Submitted on : Tuesday, November 19, 2019 - 5:26:48 PM
Last modification on : Saturday, February 1, 2020 - 1:52:41 AM


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  • HAL Id : hal-02371140, version 1


Alexandre Garcia, Pierre Colombo, Slim Essid, Florence d'Alché-Buc, Chloe Clavel. From the Token to the Review: A Hierarchical Multimodal approach to Opinion Mining. 2019 Conference on Empirical Methods in Natural Language Processing, Nov 2019, Hong-Kong, China. ⟨hal-02371140⟩



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