Name
Title | ||
|---|---|---|
A multimodal feature learning approach for sentiment analysis of social network multimedia |
Abstract | ||
|---|---|---|
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 classification results. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1007/s11042-015-2646-x | Multimedia Tools and Applications |
Keywords | Field | DocType |
Sentiment analysis,Feature learning,Micro-blogging,Twitter | Computer science,Unsupervised learning,Natural language processing,Artificial intelligence,Artificial neural network,Syntax,Language model,Social media,Sentiment analysis,Microblogging,Text corpus,Machine learning,Feature learning | Journal |
Volume | Issue | ISSN |
75 | 5 | 1380-7501 |
Citations | PageRank | References |
18 | 0.60 | 43 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| claudio baecchi | 1 | 26 | 4.87 |
| Tiberio Uricchio | 2 | 211 | 13.51 |
| Marco Bertini | 3 | 606 | 46.31 |
| Alberto Del Bimbo | 4 | 3777 | 420.44 |