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2016

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

3 years 7 months ago
A multimodal feature learning approach for sentiment analysis of social network multimedia
In this paper we investigate 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. The approach used in this work is motivated by the recent advances in: i) training language models based on neural networks that have proved to be extremely efficient when dealing with web-scale text corpora, and have shown very good performances when dealing with syntactic and semantic word similarities; ii) unsupervised learning, with neural networks, of robust visual features, that are recoverable from partial observations that may be due to occlusions or noisy and heavily modified images. We propose a novel architecture that incorporates these neural networks, testing it on several standard Twitter datasets, and showing that the approach is efficient and obtains good classifica...
Claudio Baecchi, Tiberio Uricchio, Marco Bertini,
Added 08 Apr 2016
Updated 08 Apr 2016
Type Journal
Year 2016
Where MTA
Authors Claudio Baecchi, Tiberio Uricchio, Marco Bertini, Alberto Del Bimbo
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