Name
Abstract | ||
|---|---|---|
Automatic assessment of sentiment from visual content has gained considerable attention with the increasing tendency of expressing opinions via images and videos online. This paper investigates the problem of visual sentiment analysis, which involves a high-level abstraction in the recognition process. While most of the current methods focus on improving holistic representations, we aim to utilize... |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/TMM.2018.2803520 | IEEE Transactions on Multimedia |
Keywords | Field | DocType |
Visualization,Sentiment analysis,Benchmark testing,Twitter,Art,Image recognition,Predictive models | Annotation,Abstraction,Pattern recognition,Computer science,Visualization,Sentiment analysis,Convolutional neural network,Artificial intelligence,Affect (psychology),Benchmark (computing),Machine learning | Journal |
Volume | Issue | ISSN |
20 | 9 | 1520-9210 |
Citations | PageRank | References |
17 | 0.59 | 47 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jufeng Yang | 1 | 61 | 9.97 |
| Dongyu She | 2 | 43 | 4.65 |
| Ming Sun | 3 | 91 | 16.25 |
| Ming-Ming Cheng | 4 | 1914 | 82.32 |
| Paul L. Rosin | 5 | 2559 | 254.25 |
| Liang Wang | 6 | 4317 | 243.28 |