# Sentiment Analysis from Images of Natural Disasters

arXiv: https://arxiv.org/abs/1910.04416 · HTML: https://mlsanity.com/paper/1910.04416/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Oct 2019.

## Built on by (most cited first, 10 of 10)

- [Affective Image Content Analysis: Two Decades Review and New Perspectives](https://mlsanity.com/paper/2106.16125/) (2021)
- [MEDIC: A Multi-Task Learning Dataset for Disaster Image Classification](https://mlsanity.com/paper/2108.12828/) (2021)
- [Robust Training of Social Media Image Classification Models for Rapid Disaster Response](https://mlsanity.com/paper/2104.04184/) (2021)
- [Adversarial Attacks and Defenses for Social Network Text Processing Applications: Techniques, Challenges and Future Research Directions](https://mlsanity.com/paper/2110.13980/) (2021)
- [A Named Entity Recognition and Topic Modeling-based Solution for Locating and Better Assessment of Natural Disasters in Social Media](https://mlsanity.com/paper/2405.00903/) (2024)
- [The Duo of Artificial Intelligence and Big Data for Industry 4.0: Review of Applications, Techniques, Challenges, and Future Research Directions](https://mlsanity.com/paper/2104.02425/) (2021)
- [Visual Sentiment Analysis: A Natural DisasterUse-case Task at MediaEval 2021](https://mlsanity.com/paper/2111.11471/) (2021)
- [Deep Models for Visual Sentiment Analysis of Disaster-related Multimedia Content](https://mlsanity.com/paper/2112.12060/) (2021)
- [Social Media Informatics for Sustainable Cities and Societies: An Overview of the Applications, associated Challenges, and Potential Solutions](https://mlsanity.com/paper/2412.03600/) (2024)
- [Social Media Data Mining of Human Behaviour during Bushfire Evacuation](https://mlsanity.com/paper/2512.01262/) (2025)

## Abstract (arXiv)

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.