  Sentiment analysis aims to extract and express a person's perception,
opinions and emotions towards an entity, object, product and a service,
enabling businesses to obtain feedback from the consumers. The increasing
popularity of the social networks and users' tendency towards sharing their
feelings, expressions and opinions in text, visual and audio content has opened
new opportunities and challenges in sentiment analysis. While sentiment
analysis of text streams has been widely explored in the literature, sentiment
analysis of images and videos is relatively new. This article introduces visual
sentiment analysis and contrasts it with textual sentiment analysis with
emphasis on the opportunities and challenges in this nascent research area. We
also propose a deep visual sentiment analyzer for disaster-related images as a
use-case, covering different aspects of visual sentiment analysis starting from
data collection, annotation, model selection, implementation and evaluations.
We believe such rigorous analysis will provide a baseline for future research
in the domain.
