The increasing popularity of 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 from images and videos is relatively new. This article focuses on visual sentiment analysis in a societal important domain, namely disaster analysis in social media. To this aim, we propose a deep visual sentiment analyzer for disaster related images, covering different aspects of visual sentiment analysis starting from data collection, annotation, model selection, implementation, and evaluations. For data annotation, and analyzing peoples' sentiments towards natural disasters and associated images in social media, a crowd-sourcing study has been conducted with a large number of participants worldwide. The crowd-sourcing study resulted in a large-scale benchmark dataset with four different sets of annotations, each aiming a separate task. The presented analysis and the associated dataset will provide a baseline/benchmark for future research in the domain. We believe the proposed system can contribute toward more livable communities by helping different stakeholders, such as news broadcasters, humanitarian organizations, as well as the general public.

中文翻译：

---

社交媒体中灾害图像的视觉情感分析

社交网络的日益流行以及用户在文本、视觉和音频内容中分享他们的感受、表达和观点的趋势，为情感分析带来了新的机遇和挑战。虽然文本流的情感分析在文献中得到了广泛的探索，但图像和视频的情感分析相对较新。本文重点关注社会重要领域中的视觉情感分析，即社交媒体中的灾难分析。为此，我们提出了一种针对灾害相关图像的深度视觉情感分析器，涵盖从数据收集、注释、模型选择、实现和评估开始的视觉情感分析的不同方面。用于数据标注，分析人们对自然灾害的情绪以及社交媒体中的相关图像，已经在全世界范围内对大量参与者进行了一项众包研究。众包研究产生了一个包含四组不同注释的大规模基准数据集，每组都针对一个单独的任务。所呈现的分析和相关的数据集将为该领域的未来研究提供基线/基准。我们相信提议的系统可以通过帮助不同的利益相关者（例如新闻广播公司、人道主义组织以及公众）来为更宜居的社区做出贡献。所呈现的分析和相关的数据集将为该领域的未来研究提供基线/基准。我们相信提议的系统可以通过帮助不同的利益相关者（例如新闻广播公司、人道主义组织以及公众）来为更宜居的社区做出贡献。所呈现的分析和相关的数据集将为该领域的未来研究提供基线/基准。我们相信提议的系统可以通过帮助不同的利益相关者（例如新闻广播公司、人道主义组织以及公众）来为更宜居的社区做出贡献。