Sentiment Analysis from Images of Natural Disasters
This work tackles the problem of subjective information relevance in social media for disaster response, though it appears incremental as it focuses on defining challenges and benchmarks rather than presenting new results.
The paper addresses sentiment analysis of disaster-related images from social media to understand people's opinions and emotions, aiming to improve information mining for end-users.
Social media have been widely exploited to detect and gather relevant information about opinions and events. However, the relevance of the information is very subjective and rather depends on the application and the end-users. In this article, we tackle a specific facet of social media data processing, namely the sentiment analysis of disaster-related images by considering people's opinions, attitudes, feelings and emotions. We analyze how visual sentiment analysis can improve the results for the end-users/beneficiaries in terms of mining information from social media. We also identify the challenges and related applications, which could help defining a benchmark for future research efforts in visual sentiment analysis.