Emotion Detection in Textual Information by Semantic Role Labeling and Web Mining Techniques

Abstract : Automatic emotion detection in textual information is critical for the development of intelligent interfaces in many interactive multimedia applications. In the literature, existing approaches based on keyword spotting or statistic natural language process techniques, have limited success rate in free text emotion sensing applications. In this paper, we describe a system, developed in the framework of the National ChiNan University and LORIA collaboration, that associates semantic labeling and web mining techniques, to detect several basic emotions. A common sense knowledgebase – ConceptNet – is also used in order to retrieve some additional contextual information that can be used to retrieve appropriate background images for the presentation. Our objective is to adapt a multimedia presentation by detecting emotions contained in the textual information.
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Communication dans un congrès
Third Taiwanese-French Conference on Information Technology - TFIT 2006, Mar 2006, Nancy/France, 2006
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Contributeur : Samuel Cruz-Lara <>
Soumis le : mercredi 11 octobre 2006 - 17:42:58
Dernière modification le : jeudi 11 janvier 2018 - 06:19:48
Document(s) archivé(s) le : mardi 6 avril 2010 - 19:25:50

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  • HAL Id : inria-00105649, version 1

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Cheng-Yu Lu, Jen-Shin Hong, Samuel Cruz-Lara. Emotion Detection in Textual Information by Semantic Role Labeling and Web Mining Techniques. Third Taiwanese-French Conference on Information Technology - TFIT 2006, Mar 2006, Nancy/France, 2006. 〈inria-00105649〉

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