  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.
