The increasing popularity of the social networks and users' tendency towards sharing their feelings, expressions and opinions in text, visual and audio content have opened new opportunities and challenges in sentiment analysis. While sentiment analysis of text streams has been widely explored in literature, sentiment analysis of images and videos is relatively new. This article focuses on visual sentiment analysis in a societal important domain; namely, disaster analysis in social media.
Dataset Details
The dataset consist of disaster-related images from all over the world. Each image has been manually annotated by five different people with tags related to emotion generated when viewing the image. Overall, each image is categorized into seven distinct classes, with an additoonal 14 sub-groups for a more precise categorization.
Download
| File | Description | Size | Download |
|---|---|---|---|
| sentiment.zip | The sentiment.zip archive containing the dataset. | 370MB |
All files can be viewed and downloaded in our OSF repository available here: https://osf.io/xakp2
Challenge Datasets
| Challenge | Description | Development | Test |
|---|---|---|---|
| MediaEval'21 | The datasets used for the MediaEval'21 Visual Sentiment task. |
Cite
@misc{hassan2020visual,
title = {Visual Sentiment Analysis from Disaster Images in Social Media},
author = {
Syed Zohaib Hassan and
Kashif Ahmad and
Steven Hicks and
P{\aa}l Halvorsen and
Ala Al-Fuqaha and
Nicola Conci and
ichael Riegler
},
year = 2020
}
Terms of use
The dataset is licensed under a Creative Commons Attribution 4.0 International (CC BY 4.0) License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source. This means that in all documents and papers that use or refer to the dataset or report experimental results based on the dataset, a reference to the related article needs to be added. Additionally, one should provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/
Contact
Email kahmad (at) hbku.edu.qa if you have any questions about the dataset and our research activities. We always welcome collaboration and joint research!