Investigation of the use of a multimodal feature learning approach, using neural network based models such as Skip-gram and Denoising Autoencoders, to address sentiment analysis of micro-blogging content, such as Twitter short messages, that are composed by a short text and, possibly, an image.

A multimodal feature learning approach for sentiment analysis of social network multimedia / Baecchi, Claudio; Uricchio, Tiberio; Bertini, Marco; Del Bimbo, Alberto. - In: MULTIMEDIA TOOLS AND APPLICATIONS. - ISSN 1573-7721. - ELETTRONICO. - 75:(2016), pp. 2507-2525. [10.1007/s11042-015-2646-x]

A multimodal feature learning approach for sentiment analysis of social network multimedia

BAECCHI, CLAUDIO;URICCHIO, TIBERIO;BERTINI, MARCO;DEL BIMBO, ALBERTO
2016

Abstract

Investigation of the use of a multimodal feature learning approach, using neural network based models such as Skip-gram and Denoising Autoencoders, to address sentiment analysis of micro-blogging content, such as Twitter short messages, that are composed by a short text and, possibly, an image.
75
2507
2525
Baecchi, Claudio; Uricchio, Tiberio; Bertini, Marco; Del Bimbo, Alberto
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2158/1008199
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