Name
Title | ||
|---|---|---|
When Saliency Meets Sentiment: Understanding How Image Content Invokes Emotion and Sentiment. |
Abstract | ||
|---|---|---|
Sentiment analysis is crucial for extracting social signals from social media content. Due to the prevalence of images in social media, image sentiment analysis is receiving increasing attention in recent years. However, most existing systems are black-boxes that do not provide insight on how image content invokes sentiment and emotion in the viewers. On the other hand, psychological studies have confirmed that salient objects in an image often invoke emotions. In this work, we investigate more fine-grained and more comprehensive interaction between visual saliency and visual sentiment. In particular, we partition images in several meta-level scene-type dimensions that are relevant to most images, including: open-closed, natural-manmade, indoor-outdoor, and face-noface. Facilitated by state of the art saliency detection algorithm and sentiment classification algorithm, we examine how the sentiment of the salient region(s) in an image relates to the overall sentiment of the image. The experiments on a representative image emotion dataset have shown interesting correlation between saliency and sentiment in different scene types and shed light on the mechanism of visual sentiment evocation. |
Year | Venue | DocType |
|---|---|---|
2017 | ICIP | Conference |
Volume | Citations | PageRank |
abs/1611.04636 | 0 | 0.34 |
References | Authors | |
0 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Honglin Zheng | 1 | 0 | 0.34 |
| Tianlang Chen | 2 | 12 | 3.31 |
| Jiebo Luo | 3 | 6314 | 374.00 |