Deriving Emotions and Sentiments from Visual Content: A Disaster Analysis Use Case
This work addresses the challenge of extracting emotions and sentiments from visual content, specifically for disaster analysis, but it is incremental as it applies existing deep learning methods to a new domain.
The paper tackles visual sentiment analysis, a relatively new area compared to text-based methods, by proposing a deep visual sentiment analyzer for disaster-related images, covering data collection, annotation, model selection, implementation, and evaluations to establish a baseline for future research.
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.