Name
Abstract | ||
|---|---|---|
In this work, we explore how Convolutional Neural Networks can be applied to the task of sentiment analysis in visual media. We compare four different architectures and propose a new approach where attributes that represent the main categories used for scenes description are combined with the output of the convolutional layers before the classification process. In the first dataset, composed of image tweets, we obtained accuracy improvements over previous works. The second dataset, constructed in this paper, contains only images from outdoor areas and labeled in three sentiment classes: positive, neutral and negative. Sentiment analysis of outdoor images helps to enable new services, e.g., to better uncover the semantics of areas compared to indoor images. In general, the use of the attributes improves the accuracy of the results. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1145/3243082.3243093 | WEBMEDIA'18: PROCEEDINGS OF THE 24TH BRAZILIAN SYMPOSIUM ON MULTIMEDIA AND THE WEB |
Keywords | Field | DocType |
Image Processing, Sentiment Analysis, Social Networks, Deep Learning | Data mining,Social network,Convolutional neural network,Computer science,Sentiment analysis,Image processing,Artificial intelligence,Visual media,Deep learning,Semantics,Machine learning | Conference |
Citations | PageRank | References |
0 | 0.34 | 35 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Wyverson Bonasoli | 1 | 0 | 0.34 |
| Leyza Baldo Dorini | 2 | 65 | 9.53 |
| Rodrigo Minetto | 3 | 178 | 15.01 |
| Thiago H. Silva | 4 | 189 | 22.04 |