Name
Abstract | ||
|---|---|---|
We address the challenge of sentiment analysis from visual content. In contrast to existing methods which infer sentiment or emotion directly from visual low-level features, we propose a novel approach based on understanding of the visual concepts that are strongly related to sentiments. Our key contribution is two-fold: first, we present a method built upon psychological theories and web mining to automatically construct a large-scale Visual Sentiment Ontology (VSO) consisting of more than 3,000 Adjective Noun Pairs (ANP). Second, we propose SentiBank, a novel visual concept detector library that can be used to detect the presence of 1,200 ANPs in an image. The VSO and SentiBank are distinct from existing work and will open a gate towards various applications enabled by automatic sentiment analysis. Experiments on detecting sentiment of image tweets demonstrate significant improvement in detection accuracy when comparing the proposed SentiBank based predictors with the text-based approaches. The effort also leads to a large publicly available resource consisting of a visual sentiment ontology, a large detector library, and the training/testing benchmark for visual sentiment analysis. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1145/2502081.2502282 | ACM Multimedia 2001 |
Keywords | Field | DocType |
visual concept,novel visual concept detector,automatic sentiment analysis,adjective noun pair,visual sentiment analysis,visual low-level feature,large-scale visual sentiment ontology,visual content,proposed sentibank,visual sentiment ontology,infer sentiment,sentiment analysis,ontology | Ontology,Web mining,Information retrieval,Computer science,Sentiment analysis,Noun,Natural language processing,Social multimedia,Artificial intelligence,Detector,Adjective | Conference |
Citations | PageRank | References |
222 | 7.80 | 40 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Damian Borth | 1 | 764 | 49.45 |
| Rongrong Ji | 2 | 3616 | 189.98 |
| Tao Chen | 3 | 599 | 29.93 |
| Thomas M. Breuel | 4 | 2362 | 219.10 |
| Shih-Fu Chang | 5 | 13015 | 1101.53 |